[go: up one dir, main page]

CN111638528B - Positioning method, positioning device, electronic equipment and storage medium - Google Patents

Positioning method, positioning device, electronic equipment and storage medium Download PDF

Info

Publication number
CN111638528B
CN111638528B CN202010455341.3A CN202010455341A CN111638528B CN 111638528 B CN111638528 B CN 111638528B CN 202010455341 A CN202010455341 A CN 202010455341A CN 111638528 B CN111638528 B CN 111638528B
Authority
CN
China
Prior art keywords
point cloud
cloud data
target point
original
precision map
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010455341.3A
Other languages
Chinese (zh)
Other versions
CN111638528A (en
Inventor
付向宇
万国伟
卢维欣
周尧
宋适宇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Baidu Netcom Science and Technology Co Ltd
Original Assignee
Beijing Baidu Netcom Science and Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Baidu Netcom Science and Technology Co Ltd filed Critical Beijing Baidu Netcom Science and Technology Co Ltd
Priority to CN202010455341.3A priority Critical patent/CN111638528B/en
Publication of CN111638528A publication Critical patent/CN111638528A/en
Application granted granted Critical
Publication of CN111638528B publication Critical patent/CN111638528B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G01S19/46—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being of a radio-wave signal type
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures

Landscapes

  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Electromagnetism (AREA)
  • Navigation (AREA)

Abstract

The application discloses a positioning method, a positioning device, electronic equipment and a storage medium, and relates to the technical field of automatic driving. The method comprises the following steps: in the original point cloud data, determining a preset number of original point clouds as target point clouds according to the sequence from high to low of the weights of the features of the original point clouds, and determining target point cloud data according to the target point clouds; generating a high-precision map according to the cloud data of the target point; and when receiving the request information from the equipment to be positioned, sending a high-precision map to the equipment to be positioned. Because the point cloud data in the high-precision map is not all original point cloud data, but the point cloud data with higher weight is determined according to the characteristics of the point cloud, the data volume of the point cloud data in the high-precision map can be reduced on the premise of not influencing the positioning accuracy, so that the storage space occupied by the high-precision map is reduced, and the positioning speed when the high-precision map is used for positioning is further improved.

Description

Positioning method, positioning device, electronic equipment and storage medium
Technical Field
The application relates to the technical field of automatic driving, in particular to a positioning method, a positioning device, electronic equipment and a storage medium.
Background
With the rapid rise of artificial intelligence, autopilot is increasingly receiving attention from users. In an automatic driving system, a vehicle needs to perceive the surrounding environment to determine a driving action or a driving route. Wherein the accurate positioning of the vehicle is crucial for the perception of the environment.
In the current positioning method, a laser radar is arranged on a vehicle, the laser radar is controlled to emit laser to the surrounding environment in the running process of the vehicle, and laser point cloud data around the vehicle is obtained according to the laser reflected by a reflection point. And matching the laser point cloud data around the vehicle with the original laser point cloud data in the high-precision map to acquire the positioning position of the vehicle.
Because the high-precision map contains a large amount of original laser point cloud data, the high-precision map occupies large storage space, and the high-precision map is used for positioning at a low speed.
Disclosure of Invention
The application provides a positioning method, a positioning device, electronic equipment and a storage medium, which can reduce the storage space occupied by a high-precision map and improve the positioning speed when the high-precision map is used for positioning.
A first aspect of the present application provides a positioning method, including:
Acquiring the characteristics of each original point cloud in the original point cloud data; according to the characteristics of each original point cloud, determining the weight of each original point cloud; determining a preset number of original point clouds as target point clouds according to the order of the weights from high to low, and determining target point cloud data according to the target point clouds; generating a high-precision map according to the target point cloud data; receiving request information from equipment to be positioned, wherein the request information is used for requesting a high-precision map; and sending the high-precision map to the equipment to be positioned.
In the positioning method in this embodiment, according to the characteristics of the point cloud of the original point cloud data, the characteristics with larger contribution to positioning, that is, the point cloud data corresponding to the characteristics with higher weight, are screened out, and a high-precision map is generated. On the one hand, the high-precision map is not affected, the positioning accuracy is not affected, and the data volume of point cloud data in the high-precision map can be reduced, so that the storage space occupied by the high-precision map is reduced, and the positioning speed when the high-precision map is used for positioning is further improved.
A second aspect of the present application provides a positioning method, including: acquiring point cloud data around equipment to be positioned; determining a predicted position of the device to be positioned; according to the predicted position, point cloud data at the predicted position are extracted from a high-precision map as candidate point cloud data, the point cloud data in the high-precision map are target point cloud data which are determined according to the weight of the characteristics of the point cloud in the original point cloud data obtained in advance, and the error distance of the predicted position is obtained according to the candidate point cloud data, the point cloud data around the equipment to be positioned and a characteristic matching model, wherein the characteristic matching model is used for representing the corresponding relation between the characteristic matching degree of the point cloud data and the error distance; and determining the position of the equipment to be positioned according to the predicted position and the error distance.
A third aspect of the present application provides a positioning device, comprising:
the processing module is used for acquiring the characteristics of each original point cloud from the original point cloud data, determining the weight of each original point cloud according to the characteristics of each original point cloud, determining a preset number of original point clouds as target point clouds according to the order of the weights from high to low, determining target point cloud data according to the target point clouds, and generating a high-precision map according to the target point cloud data; the receiving and transmitting module is used for receiving request information from equipment to be positioned, wherein the request information is used for requesting a high-precision map; the receiving and transmitting module is further configured to send the high-precision map to the device to be located.
The above third aspect and the possible designs of the positioning device may refer to the beneficial effects of the first aspect, which are not described herein.
A fourth aspect of the present application provides a positioning device comprising:
the radar module is used for acquiring point cloud data around the equipment to be positioned; the processing module is used for determining the predicted position of the equipment to be positioned, extracting point cloud data at the predicted position from a high-precision map according to the predicted position as candidate point cloud data, and obtaining an error distance of the predicted position according to the candidate point cloud data, the point cloud data around the equipment to be positioned and a feature matching model so as to determine the position of the equipment to be positioned according to the predicted position and the error distance; the point cloud data in the high-precision map are target point cloud data determined according to the weight of the characteristics of the point cloud in the original point cloud data acquired in advance, and the characteristic matching model is used for representing the corresponding relation between the characteristic matching degree and the error distance of the point cloud data.
The fourth aspect and the possible designs of the positioning device described above provide advantageous effects that can be seen from the relevant description of the embodiments.
A fifth aspect of the present application provides an electronic device, comprising: at least one processor and memory; the memory stores computer-executable instructions; the at least one processor executes computer-executable instructions stored by the memory to cause the electronic device to perform the positioning method of the first and second aspects described above.
A sixth aspect of the present application provides a computer readable storage medium having stored thereon computer executable instructions which, when executed by a processor, implement the positioning method of the first and second aspects described above.
Other effects of the above alternative will be described below in connection with specific embodiments.
The application discloses a positioning method, a positioning device, an electronic device and a storage medium, wherein the positioning method comprises the following steps: acquiring point cloud data around equipment to be positioned; in the original point cloud data, determining a preset number of original point clouds as target point clouds according to the sequence from high to low of the weights of the features of the original point clouds, and determining target point cloud data according to the target point clouds; generating a high-precision map according to the cloud data of the target point; and when receiving the request information from the equipment to be positioned, sending a high-precision map to the equipment to be positioned. Because the point cloud data in the high-precision map is not all original point cloud data, but the point cloud data with higher weight is determined according to the characteristics of the point cloud, and when the high-precision map is used for positioning, the characteristics of the point cloud in the point cloud data around the equipment to be positioned are matched with the characteristics of the point cloud data in the high-precision map, the positioning method can reduce the data quantity of the point cloud data in the high-precision map on the premise that the positioning accuracy is not influenced by using the high-precision map, so that the storage space occupied by the high-precision map is reduced, and the positioning speed when the high-precision map is used for positioning is improved.
Drawings
The drawings are for better understanding of the present solution and do not constitute a limitation of the present application. Wherein:
fig. 1 is a schematic view of a scenario where the positioning method provided in the present application is applicable;
FIG. 2 is a flow chart of an embodiment of a positioning method provided in the present application;
FIG. 3 is a schematic diagram of a deep learning model provided herein;
FIG. 4 is a schematic diagram of a sub-region of the high-precision map provided herein;
FIG. 5 is a schematic diagram of an obtained feature matching model provided in the present application;
FIG. 6 is a flow chart of another embodiment of a positioning method provided herein;
FIG. 7 is a schematic structural diagram of a positioning device provided in the present application;
fig. 8 is a second schematic structural diagram of the positioning device provided in the present application;
fig. 9 is a schematic structural diagram of an electronic device provided in the present application.
Detailed Description
Exemplary embodiments of the present application are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present application to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
For convenience in describing the positioning method provided in the present application, a description will be first given of a positioning method in the prior art. In robotic systems or autopilot systems, the positioning of the robot or vehicle is of paramount importance. In the prior art, positioning systems such as a Beidou system, a global positioning system (Global Positioning System, GPS) and the like can provide positioning services. GPS is affected by multipath reflection, signal shielding and other factors, and in many situations, such as building shielding or indoor, stable and reliable positioning service cannot be provided. With the continuous development of the positioning technology, the robot or the vehicle can be positioned according to the sensors, so that the stability and the accuracy of positioning can be improved.
Taking a vehicle as an example, an automatic driving system generally adopts a multi-sensor fusion mode such as GPS, laser radar, inertial navigation and the like to provide stable and reliable vehicle positioning. When the vehicle is positioned, the laser radar can be controlled to emit laser to the periphery of the vehicle, the laser reflected by the reflecting point is received, laser point cloud data around the vehicle is obtained according to the reflected laser, and the position of the vehicle is determined by combining a high-precision map. Currently, this method can provide stable centimeter-level positioning results.
The high-precision map is stored with laser point cloud data and acquisition positions of the laser point cloud data, and when the position of the vehicle is determined according to the laser point cloud data around the vehicle in combination with the high-precision map, specifically, the point cloud features of the laser point cloud data around the vehicle and the point cloud features of the laser point cloud data in the high-precision map are matched to determine the position of the vehicle. It should be appreciated that when the degree of matching of the point cloud features is greater than the degree of matching threshold, the acquisition position of the laser point cloud data in the high-precision map may be taken as the position of the vehicle.
It should be understood that the laser point cloud data stored in the high-precision map is acquired point cloud data of the vehicle in the automatic driving area, and the manner of acquiring the laser point cloud data by the vehicle is the same as the manner of acquiring the laser point cloud data by the vehicle. The method is characterized in that the acquisition vehicle can acquire laser point cloud data at a plurality of position points on a road of an automatic driving area, and then the laser point cloud data acquired by the acquisition vehicle are spliced to acquire a high-precision map. In order to obtain laser point cloud data of all positions in an automatic driving area, when the collected vehicle collects the laser point cloud data at two adjacent position points, the collected laser point clouds need to be overlapped, and then the laser point clouds can be spliced according to the same laser point cloud data, so that the laser point cloud data of all positions in the automatic driving area are obtained.
In view of the fact that the high-precision map comprises all original point cloud data collected by the collecting vehicle, the original point cloud data are all laser point cloud data collected by the collecting vehicle. Therefore, the original point cloud data in the high-precision map has large data volume and large occupied storage space, and when the vehicle is positioned by using the high-precision map, the number of point clouds required to be subjected to feature matching is also large, so that the positioning speed of the vehicle is influenced.
In order to solve the above problems, the present application provides a positioning method, which screens partial point cloud data in original point cloud data contained in a high-precision map, so that the data amount of the point cloud data contained in the high-precision map can be reduced without affecting the positioning precision when the high-precision map is used for positioning, thereby reducing the storage space occupied by the high-precision map, and improving the positioning speed when the high-precision map is used for positioning.
Fig. 1 is a schematic view of a scenario where the positioning method provided in the present application is applicable. As shown in fig. 1, the scene includes: the device and server are to be located. The server is an electronic device storing a high-precision map, and can acquire original point cloud data acquired by the acquisition vehicle and generate the high-precision map according to part of point cloud data screened in the original point cloud data. The device to be positioned may be a vehicle in an autopilot scenario, a robot in a robotic system, etc. that needs to be positioned. It is understood that the device to be positioned in the application can acquire the high-precision map from the server, and further determine the position of the device to be positioned by combining the high-precision map in the driving process. In fig. 1, a vehicle is exemplified.
It should be understood that the execution body for executing the positioning method in the present application is a positioning device, and the positioning device may be a device to be positioned, or a processor, a chip, etc. in the device to be positioned. The positioning means may be implemented by any software and/or hardware.
The positioning method provided by the application is described below with reference to specific embodiments from the perspective of interaction between the server and the device to be positioned. Fig. 2 is a flow chart of an embodiment of a positioning method provided in the present application. As shown in fig. 2, the positioning method provided in this embodiment may include:
s201, the server acquires the characteristics of each original point cloud in the original point cloud data;
s202, the server determines the weight of each original point cloud according to the characteristics of each original point cloud;
s203, the server determines a preset number of original point clouds as target point clouds according to the order of the weight from high to low, and determines target point cloud data according to the target point clouds;
s204, the server generates a high-precision map according to the target point cloud data;
s205, the equipment to be positioned sends request information to the server, wherein the request information is used for requesting the high-precision map.
S206, the server sends the high-precision map to the equipment to be positioned.
In S201 described above, in the present embodiment, the original point cloud data includes data of an original point cloud, and the data of the original point cloud may include three-dimensional coordinates and reflection values of the original point cloud in space. It should be appreciated that the origin cloud data may be acquired by the test vehicle during travel. The test vehicle can emit radar signals to the periphery, and the point cloud data of the periphery can be obtained according to the reflected radar signals. The radar signal may be a laser radar signal or a microwave radar signal, which is not limited in this embodiment. Correspondingly, the original point cloud data in the embodiment may be laser point cloud data or microwave point cloud data.
The method comprises the steps of acquiring characteristics of each original point cloud, determining weight of each original point cloud according to the characteristics of each original point cloud, and further determining a preset number of original point clouds as target point clouds according to the sequence of the weights from high to low. It should be understood that, in this embodiment, a preset number of original point clouds before the weight ranking are used as the target point clouds.
In this embodiment, the characteristics of each original point cloud may be obtained from the original point cloud data, and specifically, the attribute characteristics of the original point cloud may be extracted according to the data of each original point cloud. Wherein the attribute features are features determined by the point cloud itself data, such as three-dimensional coordinates and reflectance values. Alternatively, the obtaining the attribute feature of each original point cloud may be extracting a 32-dimensional feature vector of each original point cloud. The feature vector extraction method may be a feature vector extraction method in the prior art, or in this embodiment, a depth feature extraction module (Deep Feature Extractor) may be set in the server to extract a 32-dimensional feature vector of each original point cloud.
In S202 described above, in this embodiment, a weight determination model may be stored in advance in the server, where the weight determination model is used to characterize the correspondence between the features and weights of the point clouds, so after extracting the features of each original point cloud, the features of each point cloud may be input into the weight determination model to obtain the weights of the features of each original point cloud.
It should be appreciated that the weights of the features of each original point cloud may be used to characterize the contribution of the features of each original point cloud to the positioning. The larger the weight of the characteristics of the original point cloud is, the larger the contribution degree of the characteristics of the original point cloud to positioning is. Alternatively, the weight determining model in this embodiment may be stored in a weight module (weighting_layer), that is, the weight module is configured to obtain the weight of the feature of each original point cloud according to the feature of each original point cloud.
In S203 described above, in this embodiment, the weights of the features of the point clouds may be ordered according to the order from high to low, so as to determine the point clouds with the top rank. In this embodiment, a preset number of original point clouds before the weight ranking may be used as the target point clouds, and target point cloud data may be determined according to the target point clouds, where the data corresponding to the target point clouds is the target point cloud data. In this embodiment, because the selected target point cloud data is the point cloud data with a larger contribution to positioning in the original point cloud data, the positioning accuracy of the device to be positioned is not affected by the obtained high-precision map, that is, the positioning accuracy can be ensured by using the high-precision map.
In this embodiment, in order to improve the positioning accuracy of the precision map, since a part of the characteristics of the point cloud may be missing after the target point cloud data is screened out from the original point cloud data, in this embodiment, in order to further improve the accuracy of the characteristics of the target point cloud, that is, in order to improve the positioning accuracy of the precision map, the characteristics of the target point cloud may be supplemented by combining with other original point clouds around the characteristics of the target point cloud.
Optionally, in this embodiment, spatial features of the target point cloud may be obtained according to attribute features of the target point cloud, so as to supplement some features that may be missing from the target point cloud. The spatial features are determined by the attribute features of the target point cloud and the attribute features of the point cloud around the target point cloud, and further in this embodiment, the spatial features, the three-dimensional coordinates and the reflection values of the target point cloud are used as target point cloud data.
In this embodiment, the spatial feature of the target point cloud is obtained according to the feature determined by the attribute feature of the target point cloud and the attribute feature of the point cloud around the target point cloud. The data of the target point cloud comprises: in this embodiment, according to the three-dimensional coordinates of the target point cloud, the original point cloud within the preset area of the target point cloud is determined, and then the original point cloud within the preset area of the target point cloud is used as the point cloud around the target point cloud.
The spatial characteristics of the target point cloud may be obtained according to the attribute characteristics of the target point cloud and the attribute characteristics of the original point cloud within the target point cloud preset area, for example, the 32-dimensional feature vector of the target point cloud and the 32-dimensional feature vector of the original point cloud within the target point cloud preset area. Alternatively, the spatial feature of the target point cloud may be a 32-dimensional feature vector, and compared with the attribute feature of the target point cloud, the spatial feature in this embodiment combines the attribute features of the point cloud around the target point cloud.
The server in this embodiment stores a spatial feature model, where the spatial feature model is used to represent a correspondence between an attribute feature of a point cloud, an attribute feature of a point cloud within a preset area of the point cloud, and a spatial feature of the point cloud, that is, after the attribute feature of a target point cloud and an attribute feature of an original point cloud within the preset area of the target point cloud are input to the spatial feature model, the spatial feature of the target point cloud may be obtained. Alternatively, the spatial feature model in this embodiment may be stored in a depth feature fusion module (Deep Feature embedding).
After the spatial features of the target point cloud are acquired, the spatial features, the three-dimensional coordinates and the reflection values of the target point cloud can be used as target point cloud data. Optionally, in this implementation, the three-dimensional coordinates and the reflection value of the target point cloud may be taken as 4-dimensional features of the target point cloud, and then the 32-dimensional spatial features of the target point cloud and the 4-dimensional features of the target point cloud are spliced to obtain 36-dimensional features of the target point cloud, and then the 36-dimensional features are taken as features of the target point cloud. Correspondingly, when the point cloud characteristics of the point cloud data in the high-precision map are used for matching, the 36-dimensional characteristics can be adopted to match the characteristics of the point cloud in the point cloud data around the equipment to be positioned.
Correspondingly, the target point cloud data in the embodiment includes the spatial characteristics of the target point cloud and the three-dimensional coordinates and reflection values of the target point cloud.
Optionally, in this embodiment, the functions of the depth feature extraction module, the weight module, and the depth feature fusion module may be integrated into a deep learning model in the server, so as to implement the functions of the depth feature extraction module, the weight module, and the depth feature fusion module. That is, after the original point cloud data is input to the deep learning model, a high-precision map having the target point cloud data can be obtained.
Fig. 3 is a schematic diagram of a framework of the deep learning model provided in the present application. As shown in fig. 3, the deep learning model is integrated with a depth feature extraction module, a weight module and a depth feature fusion module, after an original point cloud is input, a 32-dimensional attribute feature of each original point cloud can be obtained through the depth feature extraction module, then a weight of the 32-dimensional attribute feature of each original point cloud can be obtained through the weight module, after target point cloud data is obtained, the attribute feature of the target point cloud data and the attribute feature of the original point cloud around the target point cloud data can be obtained, and the 32-dimensional spatial attribute of the target point cloud data can be obtained through the depth feature fusion module. And then combining the 4-dimensional characteristics (three-dimensional coordinates and reflection values) of the target point cloud to obtain 36-dimensional characteristics of the target point cloud. It should be understood that the point cloud data included in the high-precision map in the present embodiment is target point cloud data including spatial features of the target point cloud and three-dimensional coordinates and reflection values of the target point cloud.
In S204 described above, in this embodiment, after the target point cloud data is determined in the original point cloud data, a high-precision map may be generated according to the target point cloud data. Optionally, in this embodiment, a voxel (voxel) mode is used to generate the high-precision map.
Wherein, voxelization refers to storing the target point cloud in the target point cloud data in the form of a voxel. That is, the high-precision map may be divided into a plurality of sub-areas in advance, each of which has the same volume, and the target point cloud is mapped into each of the sub-areas according to the target point cloud data.
Fig. 4 is a schematic diagram of a sub-region of the high-precision map provided by the present application. As shown in fig. 4, each sub-region includes a fixed number of rows, columns, and heights in the X-axis, Y-axis, and Z-axis directions, and one sub-region is called one voxel, each voxel having a fixed volume. In this embodiment, the area covered by the target point cloud may be mapped into a plurality of sub-areas in the high-precision map, that is, it is determined in which sub-area each target point cloud is based on the three-dimensional coordinates of the target point cloud.
In this embodiment, the target point cloud in each sub-area is determined according to the three-dimensional coordinates of each target point cloud, and therefore, the relative coordinates of the target point cloud in the sub-area can be determined according to the position of the target point cloud in each sub-area in view of each sub-area having its own coordinate system. In this embodiment, the target point cloud in the sub-area may be loaded into the corresponding sub-area according to the relative coordinates of the target point cloud in the sub-area, so as to generate the high-precision map. The cloud data of the target point included in the high-precision map is: characteristics of the target point cloud, and three-dimensional coordinates and reflection values of the target point cloud.
In S205 and S206 described above, the device to be positioned may send request information to the server to request a high-precision map before the vehicle or the robot travels. After receiving the request information, the server can send a high-precision map to the equipment to be positioned, so that the equipment to be positioned determines the position of the equipment to be positioned according to the high-precision map.
Unlike the high-precision map in the prior art, the high-precision map in the prior art includes original point cloud data, and the point cloud data included in the high-precision map in the present embodiment is target point cloud data determined from the original point cloud data. The data size of the target point cloud data is smaller than that of the original point cloud data, so that the storage space occupied by the high-precision map can be reduced. In this embodiment, when the device to be positioned is positioned according to the high-precision map, the characteristics of point clouds in point cloud data around the device to be positioned and the characteristics of point clouds in the point cloud data in the high-precision map are matched, and the position corresponding to the point cloud data in the high-precision map with the matching similarity of the characteristics being greater than the similarity threshold is used as the position of the device to be positioned. It should be noted that the manner in which the apparatus to be positioned in the present embodiment determines the position of the apparatus to be positioned from the high-precision map may also be specifically referred to the description of the embodiments described below.
In the embodiment, in the original point cloud data, a preset number of original point clouds are determined as target point clouds according to the sequence from high to low of the weights of the features of the original point clouds, and the target point cloud data are determined according to the target point clouds; generating a high-precision map according to the cloud data of the target point; and when receiving the request information from the equipment to be positioned, sending a high-precision map to the equipment to be positioned. Because the point cloud data in the high-precision map is not all original point cloud data, but the point cloud data with higher weight is determined according to the characteristics of the point cloud, the data volume of the point cloud data in the high-precision map can be reduced on the premise of not influencing the positioning accuracy, so that the storage space occupied by the high-precision map is reduced, and the positioning speed when the high-precision map is used for positioning is further improved.
In order to facilitate description of the following manner in which the to-be-positioned device determines the position of the to-be-positioned device according to the high-precision map, description will be made herein with reference to fig. 5, where fig. 5 is a schematic diagram of the manner in which the server obtains the feature matching model provided in the present application:
in this embodiment, training data of the training feature matching model is original point cloud data, an acquisition position of the original point cloud data, and historical point cloud data around the device to be positioned. Optionally, in this embodiment, the infrastructure for training the feature matching model is an L3Net structure.
In this embodiment, the training data may be preprocessed before the feature matching model is trained using the training data. Optionally, in this embodiment, point cloud data of a ground point cloud in the history point cloud data may be removed, so as to train the feature matching model according to the point cloud data in the surrounding environment of the device to be positioned, so that a large amount of point cloud data is prevented from occupying features of the point cloud in the high-precision map, and accuracy of the feature matching model is improved. The method can identify the point cloud data of the ground point cloud in the historical point cloud data by adopting a random forest, a point cloud identification model and the like so as to remove the point cloud data of the ground point cloud. It should be appreciated that the recognition model is used to characterize the correspondence of point clouds to point cloud types. In this embodiment, a process of performing point cloud identification by using a random forest or the like will not be described in detail.
The original point cloud data in this embodiment includes multiple frames of original point cloud data, that is, original point cloud data collected at multiple position points by the collection vehicle, and the collection position of the original point cloud data also includes the collection position of each frame of original point cloud data. When the training data is preprocessed, a plurality of sample positions corresponding to the acquisition positions of the original point cloud of each frame can be generated according to the acquisition positions of the original point cloud data of each frame. For example, for acquisition position a, a plurality of sample positions corresponding to acquisition position a may be generated. Optionally, in this embodiment, at least one error distance may be randomly generated, and then a sum or a difference between the acquisition position of the frame original point cloud and each error distance is used as one sample position, so as to generate a plurality of sample positions.
Optionally, the acquisition position of each frame of original point cloud in the embodiment may be a three-dimensional coordinate in the high-precision map, and the error distance may include an error distance of an abscissa, an error distance of an ordinate, and an error distance of a Z coordinate. Illustratively, if at least one error distance is (+3, +2, -1) and (+2, +1, 0) and the acquisition position of the original point cloud is (+200, +300, +10), then the corresponding plurality of sample positions of the frame original point cloud may be (+203, +302, +9) and (+202, +301, +10).
In this embodiment, original point cloud data in a preset area range of each sample position may be obtained from multiple frames of original point cloud data, and the original point cloud data is randomly sampled in the history point cloud data to obtain history point cloud data with the same coverage area as the original point cloud data in the preset area.
In this embodiment, the original point cloud data within the preset area range of each sample position and the historical point cloud data same as the preset area coverage range may be used as training data, and the original point cloud data within the preset area range of each sample position is identified with each sample position and a corresponding acquisition position. In the training process, the error distance between each sample position and the acquisition position in each training can be obtained, and then parameters in the feature matching model are adjusted according to the error distance until the error distance between each sample position and the acquisition position is smaller than a distance threshold value, so that the feature matching model in the embodiment is obtained, wherein the feature matching model is used for representing the corresponding relation between the feature matching degree and the error distance of the point cloud data, and the error distance is the error distance between the acquisition position of the point cloud data and the position of the equipment to be positioned.
The above is a process of obtaining the feature matching model by the server in this embodiment, and when the device to be positioned, which is improved in the above embodiment, is to be positioned, the matching needs to be performed according to the point cloud data around the device to be positioned and the point cloud data in the high-precision map, so as to position the device to be positioned. In the matching process, feature extraction and matching are also needed to be carried out on point cloud data, so that the matching speed is low. And when positioning is performed based on the matching similarity and the similarity threshold, the determination of the similarity threshold is critical, if the similarity threshold is larger, the position of the equipment to be positioned cannot be determined, and if the similarity threshold is smaller, the position of the equipment to be positioned cannot be accurately determined.
In this embodiment, in order to improve positioning accuracy when using the high-precision map, the feature matching model may be stored in advance in the device to be positioned, or may be requested from the server when requesting the high-precision map from the server. The feature matching model is used for representing the corresponding relation between the feature matching degree of the point cloud data and the error distance, and further, the error distance of the point cloud data around the equipment to be positioned and the point cloud data in the high-precision map can be determined according to the feature matching degree of the point cloud data, and further, the accuracy of positioning by using the high-precision map can be improved.
The following describes a manner of determining the position of the device to be positioned according to the high-precision map after the device to be positioned obtains the high-precision map from the angle of the device to be positioned. Fig. 6 is a flowchart of another embodiment of a positioning method provided in the present application. As shown in fig. 6, the positioning method provided in this embodiment may include:
s601, acquiring point cloud data around equipment to be positioned.
S602, determining the predicted position of the equipment to be positioned.
And S603, extracting point cloud data at the predicted position from the high-precision map as candidate point cloud data according to the predicted position.
S604, obtaining the error distance of the predicted position according to the candidate point cloud data, the point cloud data around the equipment to be positioned and the feature matching model, wherein the feature matching model is used for representing the corresponding relation between the feature matching degree of the point cloud data and the error distance.
S605, determining the position of the equipment to be positioned according to the predicted position and the error distance.
It should be understood that in the above step S601, during the movement or running of the device to be located, the radar signal may be controlled to be transmitted to the periphery of the device to be located, and the point cloud data around the device to be located may be obtained according to the reflected radar signal. The radar signal may be a laser radar signal or a microwave radar signal, and correspondingly, the point cloud data around the device to be positioned in the embodiment may be laser point cloud data or microwave point cloud data.
In S602, the predicted position of the device to be located may be the position of the predicted device to be located when the surrounding point cloud data is acquired. Alternatively, the predicted position may be determined by the GPS positioning system, and in view of the low accuracy of the position of the device to be positioned determined by the GPS positioning system, the position of the device to be positioned determined by the GPS positioning system may be taken as the predicted position.
In S603 described above, since the acquisition position of the point cloud data, that is, the acquisition position of the target point cloud data is included in the high-precision map, the point cloud data at the predicted position can be extracted from the high-precision map as the candidate point cloud data. In this embodiment, the above feature matching model may be combined to determine an error distance corresponding to the feature matching degree of the point cloud data around the device to be positioned and the candidate point cloud data, so as to determine an accurate position of the device to be positioned.
In S604, in this embodiment, the candidate point cloud data and the point cloud data around the device to be positioned may be input to the feature matching model to obtain an error distance corresponding to the feature matching degree of the point cloud data and the candidate point cloud data.
Alternatively, candidate point cloud data may be screened in order to reduce the computational effort of the feature matching model. The candidate point cloud data also comprises the three-dimensional coordinates of the point cloud. In this embodiment, the number of point clouds around the to-be-positioned device existing in the preset area range of each point cloud in the candidate point cloud data may be determined according to the three-dimensional coordinates of each point cloud in the candidate point cloud data and the three-dimensional coordinates of each point cloud in the point cloud data around the to-be-positioned device. In this embodiment, the number of point clouds around the to-be-positioned device existing within 1.5 meters around each point cloud in the candidate point cloud data may be obtained, that is, the number of point clouds around the to-be-positioned device in which the distance between each point cloud around each point cloud in the candidate point cloud data and each point cloud is less than or equal to 1.5 meters is obtained. And deleting the point clouds in the candidate point cloud data with the number of the point clouds around the equipment to be positioned being smaller than the number threshold value in 1.5 m around each point cloud, so as to obtain the point clouds in the candidate point cloud data with the number being larger than the number threshold value.
In this embodiment, the error distance of the predicted position may be obtained according to the point cloud data of the point cloud in the candidate point cloud data whose number is greater than the number threshold, the point cloud data around the device to be positioned, and the feature matching model. That is, the point cloud data of the point clouds in the candidate point cloud data with the number larger than the number threshold and the point cloud data around the equipment to be positioned are input into the feature matching model, so that the error distance of the predicted position is obtained.
In S605, after determining the error distance corresponding to the predicted position of the device to be positioned, the position of the device to be positioned may be determined according to the predicted position and the error distance. Optionally, the predicted position of the device to be positioned is a three-dimensional coordinate in the high-precision map, and the error distance is an error distance of the three-dimensional coordinate, such as an error distance of an abscissa, an error distance of an ordinate, and an error distance of a Z-coordinate. In this embodiment, the position of the device to be positioned may be determined according to the three-dimensional coordinates of the predicted position in the high-precision map and the error distance. Specifically, the abscissa, the ordinate and the Z coordinate of the three-dimensional coordinate of the predicted position in the high-precision map may be added or subtracted with the error distance of the corresponding abscissa, the error distance of the ordinate and the error distance of the Z coordinate, respectively, so as to obtain the accurate three-dimensional coordinate of the device to be positioned in the high-precision map, i.e. the position of the device to be positioned.
In the positioning method provided by the embodiment, the feature matching model can be stored in the equipment to be positioned in advance, and is used for representing the corresponding relation between the feature matching degree of the point cloud data and the error distance, so that the error distance of the point cloud data around the equipment to be positioned and the point cloud data in the high-precision map can be determined according to the feature matching degree of the point cloud data around the equipment to be positioned, and the position of the equipment to be positioned is further determined.
The feature matching model stored in the device to be positioned can be acquired by a server requested by the device to be positioned. Optionally, before the device to be located in this embodiment runs, request information may be sent to the server to request the feature matching model. It should be understood that the request information may be one request information for requesting a high-precision map with the above-described device to be positioned.
After receiving the request information, the server may send the feature matching model to the device to be located. The server may be obtained by deep learning according to the original point cloud data and the history point cloud data around the to-be-positioned device obtained by the to-be-positioned device, which is specifically described in the related description of fig. 6.
In this embodiment, a feature matching model for representing a correspondence between feature matching degree and error distance of point cloud data may be trained according to original point cloud data, an acquisition position of the original point cloud data, and historical point cloud data around a device to be positioned, so that a problem that a set similarity threshold in the prior art affects positioning accuracy may be avoided, and accuracy of positioning using a high-accuracy map may be improved.
Fig. 7 is a schematic structural diagram of a positioning device provided in the present application. It should be understood that the positioning device may be a server in the above embodiment, for performing the actions of the server. As shown in fig. 7, the positioning device 700 includes: a processing module 701 and a transceiver module 702.
The processing module 701 is configured to obtain a feature of each original point cloud from the original point cloud data, determine a weight of each original point cloud according to the feature of each original point cloud, determine a preset number of original point clouds as target point clouds according to a sequence from high to low of the weight, determine target point cloud data according to the target point clouds, and generate a high-precision map according to the target point cloud data;
the transceiver module 702 is configured to receive request information from a device to be located, where the request information is used to request a high-precision map.
The transceiver module 702 is further configured to send the high-precision map to the device to be located.
In one design, the processing module 702 is specifically configured to determine a weight of each original point cloud according to a feature of each original point cloud and a weight determining model, where the weight determining model is used to characterize a correspondence between the feature and the weight of the point cloud.
In one design, the point cloud data includes three-dimensional coordinates and reflection values of the point cloud, each original point cloud is characterized by an attribute characteristic of the original point cloud, and the attribute characteristic is a characteristic determined by the point cloud itself data.
The processing module 702 is specifically configured to obtain a spatial feature of the target point cloud according to an attribute feature of the target point cloud, where the spatial feature is a feature determined by the attribute feature of the target point cloud and an attribute feature of a point cloud around the target point cloud; and taking the spatial characteristics, the three-dimensional coordinates and the reflection value of the target point cloud as target point cloud data.
In one design, the processing module 702 is specifically configured to determine an original point cloud within a preset area range of the target point cloud according to three-dimensional coordinates of the target point cloud; and acquiring the spatial characteristics of the target point cloud according to the attribute characteristics of the target point cloud and the attribute characteristics of the original point cloud within the range of the target point cloud preset area.
In one design, the processing module 702 is specifically configured to obtain a spatial feature of the target point cloud according to the attribute feature of the target point cloud, the attribute feature of the original point cloud within the preset area of the target point cloud, and the spatial feature model, where the spatial feature model is used to characterize a correspondence between the attribute feature of the target point cloud, the attribute feature of the point cloud within the preset area of the target point cloud, and the spatial feature of the point cloud.
In one design, the target point cloud is multiple, and the high-precision map is divided into multiple subareas, and the volumes of all subareas are the same.
The processing module 702 is specifically configured to map an area covered by the target point cloud to a plurality of sub-areas in the high-precision map; determining the target point cloud in each sub-region according to the three-dimensional coordinates of each target point cloud; acquiring relative coordinates of a target point cloud in each sub-area in the sub-area; and loading the target point cloud into the corresponding sub-region to generate a high-precision map.
In one design, the processing module 702 is further configured to train a feature matching model according to the original point cloud data, the acquisition position of the original point cloud data, and the historical point cloud data around the device to be positioned as training data, where the feature matching model is used to characterize a correspondence between a feature matching degree of the point cloud data and an error distance, and the error distance is an error distance between the acquisition position of the point cloud data and the position of the device to be positioned.
In one design, the original point cloud data includes point cloud data of multiple frames of original point clouds, and an acquisition position of the original point cloud data is an acquisition position of each frame of original point cloud data.
The processing module 702 is specifically configured to generate a plurality of sample positions corresponding to the acquisition positions of each frame of original point cloud data according to the acquisition positions of each frame of original point cloud data; acquiring original point cloud data in a preset area range of each sample position from multi-frame original point cloud data; acquiring historical point cloud data which are the same as the coverage range of a preset area; and training the original point cloud data in the preset area range of each sample position and the historical point cloud data which are the same as the coverage range of the preset area as training data until the error distance between each sample position and the acquisition position is smaller than a distance threshold value, and obtaining a feature matching model.
In one design, the processing module 702 is specifically configured to randomly generate at least one error distance; and taking the sum or difference of the acquisition position of the original point cloud data of each frame and each error distance as one sample position to generate a plurality of sample positions.
In one design, the processing module 702 is further configured to remove point cloud data of a ground point cloud in the historical point cloud data.
In one design, the processing module 702 is specifically configured to identify a ground point cloud in the historical point cloud according to a point cloud identification model to remove point cloud data of the ground point cloud, where the identification model is used to characterize a correspondence between the point cloud and a point cloud type.
In one design, the transceiver module 701 is further configured to receive second request information from the device to be located, and send the feature matching model to the device to be located, where the second request information is used for the feature matching model.
Fig. 8 is a schematic structural diagram of a positioning device provided in the present application. The positioning device may be the device to be positioned in the above embodiment, or a processor, a chip, or the like in the device to be positioned. As shown in fig. 8, the positioning device 800 includes: a radar module 801, a processing module 802 and a transceiver module 803.
The radar module 801 is configured to acquire point cloud data around a device to be located.
A processing module 802, configured to determine a predicted position of the device to be located, extract, according to the predicted position, point cloud data at the predicted position from a high-precision map as candidate point cloud data, and obtain, according to the candidate point cloud data, point cloud data around the device to be located, and a feature matching model, an error distance of the predicted position, so as to determine a position of the device to be located according to the predicted position and the error distance; the point cloud data in the high-precision map are target point cloud data determined according to the weight of the characteristics of the point cloud in the original point cloud data acquired in advance, and the characteristic matching model is used for representing the corresponding relation between the characteristic matching degree and the error distance of the point cloud data.
In one design, a processing module 802 is specifically configured to determine, according to a three-dimensional coordinate of each point cloud in the candidate point cloud data and a three-dimensional coordinate of each point cloud in point cloud data around the to-be-positioned device, a number of point clouds around the to-be-positioned device existing in a preset area range of each point cloud in the candidate point cloud data; determining point clouds in candidate point cloud data with the number greater than a number threshold; and acquiring the error distance of the predicted position according to the point cloud data of the point clouds in the candidate point cloud data with the quantity larger than the quantity threshold, the point cloud data around the equipment to be positioned and the feature matching model.
In one design, the predicted position is a three-dimensional coordinate of the predicted position in a high-precision map, and the error distance is an error distance of the three-dimensional coordinate.
The processing module 802 is specifically configured to determine a position of the device to be located by using three-dimensional coordinates and an error distance of the predicted position in the high-precision map.
In one design, transceiver module 803 is configured to send request information to a server and receive a high-precision map and feature matching model from the server, the request information being used to request the high-precision map and feature matching model.
The positioning device provided in this embodiment is similar to the principle and technical effects achieved by the above positioning method, and will not be described herein.
According to embodiments of the present application, an electronic device and a readable storage medium are also provided. According to an embodiment of the present application, there is also provided a computer program product comprising: a computer program stored in a readable storage medium, from which at least one processor of an electronic device can read, the at least one processor executing the computer program causing the electronic device to perform the solution provided by any one of the embodiments described above. Fig. 9 is a schematic structural diagram of an electronic device provided in the present application. As shown in fig. 9, a block diagram of an electronic device according to a positioning method according to an embodiment of the present application is shown. Electronic devices are intended to represent various forms of digital computers, processors, or chips. Such as an on-board computer, an on-board terminal device, a vehicle center control computer, and a chip of a processor in the vehicle, a robot, or a processor or chip in the robot, etc. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the application described and/or claimed herein.
As shown in fig. 9, the electronic device includes: one or more processors 901, memory 902, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the electronic device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In other embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple electronic devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). In fig. 9, a processor 901 is taken as an example.
Memory 902 is a non-transitory computer-readable storage medium provided herein. Wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the positioning method provided herein. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to perform the positioning method provided by the present application.
The memory 902 is used as a non-transitory computer readable storage medium for storing non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the positioning methods in the embodiments of the present application. The processor 901 executes various functional applications of the server and sample processing, i.e., implements the positioning method in the above-described method embodiments, by running non-transitory software programs, instructions, and modules stored in the memory 902.
Memory 902 may include a storage program area and a storage sample area, wherein the storage program area may store an operating system, at least one application program required for a function; the stored sample area may store samples or the like created according to the use of the electronic device for performing the positioning method. In addition, the memory 902 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 902 optionally includes memory remotely located relative to the processor 901, which may be connected to the electronic device for performing the positioning method via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Optionally, when the electronic device performing the positioning method is a device to be positioned, the electronic device may further include: input means 903, output means 904 and radar 905. The processor 901, memory 902, input devices 903, and output devices 904 may be connected by a bus or other means, for example in fig. 9. The radar 905 may be a laser radar, a microwave radar, or the like, and the radar 905 may be disposed at the head, the tail, both sides, or other positions of the vehicle, for performing the actions of the radar module. The processor 901, the memory 902, the input device 903, the output device 904, and the radar 905 may be connected by a bus, which is illustrated in fig. 9.
The input device 903 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the electronic device for performing the positioning method, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointer stick, one or more mouse buttons, a track ball, a joystick, etc. The output means 904 may include a display device, auxiliary lighting means (e.g., LEDs), tactile feedback means (e.g., vibration motors), and the like. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device may be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASIC (application specific integrated circuit), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive samples and instructions from, and transmit samples and instructions to, a storage system, at least one input device, and at least one output device.
These computing programs (also referred to as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or samples to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or samples to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a sample server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system may be interconnected by any form or medium of digital sample communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, provided that the desired results of the technical solutions disclosed in the present application can be achieved, and are not limited herein.
The above embodiments do not limit the scope of the application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application are intended to be included within the scope of the present application.

Claims (14)

1. A positioning method, comprising:
acquiring the characteristics of each original point cloud in the original point cloud data;
According to the characteristics of each original point cloud, determining the weight of each original point cloud;
determining a preset number of original point clouds as target point clouds according to the order of the weights from high to low; the characteristic of each original point cloud is an attribute characteristic of the original point cloud, and the attribute characteristic is a characteristic determined through data of the point cloud;
acquiring spatial characteristics of the target point cloud according to the attribute characteristics of the target point cloud, wherein the spatial characteristics are determined by the attribute characteristics of the target point cloud and the attribute characteristics of the point cloud around the target point cloud;
taking the spatial characteristics, the three-dimensional coordinates and the reflection value of the target point cloud as the target point cloud data;
generating a high-precision map according to the target point cloud data;
receiving request information from equipment to be positioned, wherein the request information is used for requesting a high-precision map;
and sending the high-precision map to the equipment to be positioned.
2. The method of claim 1, wherein determining the weight of each source point cloud based on the characteristics of each source point cloud comprises:
and determining the weight of each original point cloud according to the characteristics of each original point cloud and a weight determining model, wherein the weight determining model is used for representing the corresponding relation between the characteristics and the weights of the point cloud.
3. The method according to claim 1, wherein the obtaining the spatial feature of the target point cloud according to the attribute feature of the target point cloud comprises:
determining an original point cloud in a preset area range of the target point cloud according to the three-dimensional coordinates of the target point cloud;
and acquiring the spatial characteristics of the target point cloud according to the attribute characteristics of the target point cloud and the attribute characteristics of the original point cloud within the range of the preset area of the target point cloud.
4. The method according to claim 3, wherein the generating the spatial feature of the target point cloud according to the attribute feature of the target point cloud and the attribute feature of the original point cloud within the target point cloud preset area range includes:
and acquiring the spatial characteristics of the target point cloud according to the attribute characteristics of the target point cloud, the attribute characteristics of the original point cloud in the preset area range of the target point cloud and a spatial characteristic model, wherein the spatial characteristic model is used for representing the corresponding relationship among the attribute characteristics of the point cloud, the attribute characteristics of the point cloud in the preset area range of the point cloud and the spatial characteristics of the point cloud.
5. The method according to any one of claims 1-4, wherein the target point cloud is a plurality of, the high-precision map is divided into a plurality of sub-areas, each sub-area having the same volume, and the generating the high-precision map according to the target point cloud data includes:
Mapping the area covered by the target point cloud into a plurality of subareas in the high-precision map;
determining the target point cloud in each sub-region according to the three-dimensional coordinates of each target point cloud;
acquiring the relative coordinates of the target point cloud in each sub-area in the sub-area;
and loading the target point cloud into a corresponding sub-region to generate the high-precision map.
6. The method according to claim 1, wherein the method further comprises:
and training a feature matching model according to the original point cloud data, the acquisition position of the original point cloud data and the historical point cloud data around the equipment to be positioned as training data, wherein the feature matching model is used for representing the corresponding relation between the feature matching degree of the point cloud data and the error distance, and the error distance is the error distance between the acquisition position of the point cloud data and the position of the equipment to be positioned.
7. The method of claim 6, wherein the raw point cloud data comprises point cloud data of a plurality of frames of raw point cloud, the acquisition location of the raw point cloud data comprises an acquisition location of each frame of raw point cloud data, and the training the feature matching model based on the raw point cloud data, the acquisition location of the raw point cloud data, and the history point cloud data around the device to be located as training data comprises:
Generating a plurality of sample positions corresponding to the acquisition positions of the original point cloud data of each frame according to the acquisition positions of the original point cloud data of each frame;
acquiring original point cloud data in a preset area range of each sample position from the multi-frame original point cloud data;
acquiring historical point cloud data which are the same as the coverage range of the preset area;
and training the original point cloud data in the preset area range of each sample position and the historical point cloud data which are the same as the coverage range of the preset area as training data until the error distance between each sample position and the acquisition position is smaller than a distance threshold value, and acquiring the feature matching model.
8. The method of claim 7, wherein generating a plurality of sample locations corresponding to the acquisition locations of each frame of raw point cloud data based on the acquisition locations of each frame of raw point cloud data comprises:
randomly generating at least one error distance;
and taking the sum or difference of the acquisition position of the original point cloud data of each frame and each error distance as one sample position to generate a plurality of sample positions.
9. The method of claim 7, further comprising, prior to the acquiring the historical point cloud data that is the same as the predetermined area coverage:
And removing the point cloud data of the ground point cloud in the historical point cloud data.
10. The method of claim 9, wherein the removing the point cloud data of the ground point cloud from the historical point cloud data comprises:
and identifying the ground point cloud in the history point cloud according to a point cloud identification model so as to remove the point cloud data of the ground point cloud, wherein the identification model is used for representing the corresponding relation between the point cloud and the point cloud type.
11. The method according to any one of claims 6-10, further comprising:
receiving second request information from the equipment to be positioned, wherein the second request information is used for the feature matching model;
and sending the feature matching model to the equipment to be positioned.
12. A positioning device, comprising:
the processing module is used for acquiring the characteristics of each original point cloud in original point cloud data, determining the weight of each original point cloud according to the characteristics of each original point cloud, determining a preset number of original point clouds as target point clouds according to the order of the weight from high to low, wherein the characteristics of each original point cloud are the attribute characteristics of the original point clouds, the attribute characteristics are the characteristics determined through the data of the point clouds, the spatial characteristics of the target point clouds are acquired according to the attribute characteristics of the target point clouds, the spatial characteristics are the characteristics determined through the attribute characteristics of the target point clouds and the attribute characteristics of the point clouds around the target point clouds, taking the spatial characteristics, the three-dimensional coordinates and the reflection values of the target point clouds as the target point cloud data, and generating a high-precision map according to the target point cloud data;
The receiving and transmitting module is used for receiving request information from equipment to be positioned, wherein the request information is used for requesting a high-precision map;
the receiving and transmitting module is further configured to send the high-precision map to the device to be located.
13. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-11.
14. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-11.
CN202010455341.3A 2020-05-26 2020-05-26 Positioning method, positioning device, electronic equipment and storage medium Active CN111638528B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010455341.3A CN111638528B (en) 2020-05-26 2020-05-26 Positioning method, positioning device, electronic equipment and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010455341.3A CN111638528B (en) 2020-05-26 2020-05-26 Positioning method, positioning device, electronic equipment and storage medium

Publications (2)

Publication Number Publication Date
CN111638528A CN111638528A (en) 2020-09-08
CN111638528B true CN111638528B (en) 2023-05-30

Family

ID=72331030

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010455341.3A Active CN111638528B (en) 2020-05-26 2020-05-26 Positioning method, positioning device, electronic equipment and storage medium

Country Status (1)

Country Link
CN (1) CN111638528B (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112543859B (en) * 2020-10-28 2022-07-15 华为技术有限公司 Positioning method, positioning device, electronic equipment and storage medium
WO2022088104A1 (en) * 2020-10-30 2022-05-05 华为技术有限公司 Method and apparatus for determining point cloud set corresponding to target object
CN112859136B (en) * 2021-01-19 2022-03-25 腾讯科技（深圳）有限公司 Positioning method and related device
CN112462372B (en) * 2021-01-29 2021-06-15 北京主线科技有限公司 Vehicle positioning method and device
CN113640779B (en) * 2021-10-15 2022-05-03 北京一径科技有限公司 Radar failure determination method and device, and storage medium
CN115526936B (en) * 2022-11-29 2023-04-28 长沙智能驾驶研究院有限公司 Training method of positioning model and point cloud data positioning method and device

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109141446B (en) * 2018-07-04 2021-11-12 阿波罗智能技术(北京)有限公司 Method, apparatus, device and computer-readable storage medium for obtaining map
CN109064506B (en) * 2018-07-04 2020-03-13 百度在线网络技术（北京）有限公司 High-precision map generation method and device and storage medium
CN108921947B (en) * 2018-07-23 2022-06-21 百度在线网络技术（北京）有限公司 Method, device, equipment, storage medium and acquisition entity for generating electronic map
CN110873568B (en) * 2018-08-30 2021-02-23 百度在线网络技术（北京）有限公司 High-precision map generation method and device and computer equipment
CN109459734B (en) * 2018-10-30 2020-09-11 百度在线网络技术（北京）有限公司 Laser radar positioning effect evaluation method, device, equipment and storage medium
CN110084272B (en) * 2019-03-26 2021-01-08 哈尔滨工业大学（深圳） Cluster map creation method and repositioning method based on cluster map and position descriptor matching
CN110794392B (en) * 2019-10-15 2024-03-19 上海创昂智能技术有限公司 Vehicle positioning method and device, vehicle and storage medium
CN111144483B (en) * 2019-12-26 2023-10-17 歌尔股份有限公司 Image feature point filtering method and terminal

Also Published As

Publication number Publication date
CN111638528A (en) 2020-09-08

Similar Documents

Publication Publication Date Title
CN111638528B (en) Positioning method, positioning device, electronic equipment and storage medium
US11615605B2 (en) Vehicle information detection method, electronic device and storage medium
CN111968229B (en) High-precision map making method and device
CN111401208B (en) Obstacle detection method and device, electronic equipment and storage medium
CN109270545B (en) Positioning true value verification method, device, equipment and storage medium
CN110556012B (en) Lane positioning method and vehicle positioning system
CN111596298B (en) Target object positioning method, device, equipment and storage medium
CN111324115B (en) Obstacle position detection fusion method, obstacle position detection fusion device, electronic equipment and storage medium
CN111553844B (en) Method and device for updating point cloud
US20210319261A1 (en) Vehicle information detection method, method for training detection model, electronic device and storage medium
CN112184914B (en) Method and device for determining three-dimensional position of target object and road side equipment
CN111079079B (en) Data correction method, device, electronic equipment and computer readable storage medium
CN112147632A (en) Method, device, equipment and medium for testing vehicle-mounted laser radar perception algorithm
US20220027705A1 (en) Building positioning method, electronic device, storage medium and terminal device
CN111721281B (en) Position identification method and device and electronic equipment
CN111797745B (en) Training and predicting method, device, equipment and medium for object detection model
CN112101209A (en) Method and apparatus for determining a world coordinate point cloud for roadside computing devices
CN111310840A (en) Data fusion processing method, device, equipment and storage medium
CN113844463B (en) Vehicle control method and device based on automatic driving system and vehicle
EP3904829B1 (en) Method and apparatus for generating information, device, medium and computer program product
CN114186007A (en) High-precision map generation method and device, electronic equipment and storage medium
CN111949816A (en) Positioning processing method and device, electronic equipment and storage medium
CN111666876A (en) Method and device for detecting obstacle, electronic equipment and road side equipment
CN111932611A (en) Object position acquisition method and device
CN110866504A (en) Method, device and equipment for acquiring marked data

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant