CN112414415B - High-precision point cloud map construction method - Google Patents
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
- G01C21/32—Structuring or formatting of map data
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3407—Route searching; Route guidance specially adapted for specific applications
- G01C21/3415—Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
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- 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
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Abstract
The embodiment of the invention relates to a high-precision point cloud map construction method, which comprises the following steps: receiving first sensor data sent by a terminal; judging whether the first sensor data accords with a data qualification judging rule or not; when the first sensor data accords with the data qualification judging rule, obtaining second sensor data according to the sensor data; constructing a point cloud map according to the second sensor data; the point cloud map comprises first positioning data; and when the point cloud map meets the precision condition, the distortion condition, the smoothness condition, the continuity condition and the integrity condition, determining that the point cloud map passes the verification. The high-precision point cloud map construction method provided by the embodiment of the invention improves the quality of the point cloud map and can simultaneously execute construction of a plurality of point cloud map tasks.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to a high-precision point cloud map construction method.
Background
At present, more point cloud map construction methods are applied to the automatic driving industry and can be roughly divided into two types, namely, a point cloud map construction method based on offline data and a point cloud map construction method based on online data.
According to the point cloud map construction method based on the offline data, if the point cloud map construction is finished offline based on the vehicle end, the performance requirement on a vehicle-mounted processor is high, a large map is difficult to process, the quality and the error of the map cannot be guaranteed, and a plurality of point cloud maps cannot be constructed simultaneously; if the point cloud map construction is finished offline based on the local platform type machine, complicated processes such as data downloading, software starting and result data uploading are involved, more human intervention is needed, the degree of automation is low, the efficiency is reduced, the simultaneous construction of a plurality of scene point cloud maps cannot be realized, and the waste of manual resources and local computing resources is seriously caused.
According to the point cloud map construction method based on the online data, because the point cloud map construction tasks are all completed in the vehicle-mounted processor, the performance requirement on the vehicle-mounted processor is high, a large map is difficult to process, the map quality cannot be guaranteed, verification of the point cloud map is absent, the later data accumulation is not friendly, and a plurality of point cloud map construction tasks cannot be simultaneously realized due to the limitation of vehicles and scenes.
Disclosure of Invention
The invention aims at overcoming the defects of the prior art, and provides a high-precision point cloud map construction method which is executed by a cloud server, can realize construction of a plurality of point cloud map tasks at the same time, and overcomes the difficulty that a large map is difficult to process. And by verifying the constructed point cloud map, the quality of the point cloud map is improved.
To achieve the above object, in a first aspect, the present invention provides a high-precision point cloud map construction method, including:
receiving first sensor data sent by a terminal; the first sensor data comprises point cloud data, inertial navigation data, wheel speed meter data, GPS positioning data and image data;
generating a map task construction request message for constructing a map after receiving the first sensor data;
acquiring the current memory occupation amount and the current map task construction number according to the map task construction request message;
when the current memory occupation amount is smaller than the preset memory occupation amount and the current map task construction number is smaller than a preset map task construction threshold value, judging whether the first sensor data accords with a data qualification judging rule or not;
when the first sensor data accords with the data qualification judging rule, obtaining second sensor data according to the point cloud data, the inertial navigation data, the wheel speed meter data, the GPS positioning data and the image data;
constructing a point cloud map according to the second sensor data; the point cloud map includes a plurality of points, each of the points having first positioning data;
verifying the point cloud map, when a flag bit of a dynamic carrier phase difference technology RTK represents that a signal is normal, comparing the first positioning data with second positioning data obtained through a real-time dynamic carrier phase difference technology RTK, and when the difference value between the first positioning data and the second positioning data is not greater than a preset threshold value, determining that the point cloud map meets an accuracy condition;
generating a local path with preset length according to the inertial navigation data and the wheel speed meter data, comparing the local path with local paths in a point cloud map with the same preset length, and determining that the point cloud map meets distortion conditions when the difference value of the curvatures of the points of the local path and the local path in the point cloud map with the same preset length is not larger than a preset difference value threshold;
judging whether jump exists in the connecting lines among a plurality of first positioning data in the point cloud map, and determining that the point cloud map meets the smoothness condition when no jump exists;
judging whether the distance between any two adjacent points corresponding to the first positioning data in the point cloud map is not more than a preset distance threshold value, and determining that the point cloud map meets a continuity condition when the distance is not more than the preset distance threshold value;
judging whether the point is complete, and when the point is complete, determining that the point cloud map meets the complete condition;
and when the point cloud map accords with the precision condition, the distortion condition, the smoothness condition, the continuity condition and the integrity condition, determining that the point cloud map passes verification.
Preferably, the first sensor data sent by the receiving terminal specifically includes:
receiving multi-frame original sensor data sent by a terminal; each frame of the original sensor data comprises a frame header;
judging whether the transmission of the multi-frame original sensor data is finished or not according to the frame header;
and when the transmission is finished, splicing the original sensor data of the multiple frames according to the frame head to obtain first sensor data.
Preferably, the first sensor data sent by the receiving terminal includes:
the sensor receives a map acquisition instruction sent by a terminal and acquires data according to a preset route and a preset mode according to the map acquisition instruction;
after the data acquisition is completed, the terminal indicates the data acquisition is completed through the export instruction.
Preferably, the determining whether the first sensor data meets the data qualification determining rule specifically includes:
judging whether the format of the first sensor data is a preset format or not;
and when the format of the first sensor data does not accord with the preset format, generating a first prompt message for indicating to reacquire the first sensor data, and sending the first prompt message to a terminal.
Preferably, the determining whether the first sensor data meets the data qualification determining rule specifically includes:
judging whether the data quality of the first sensor meets the requirement or not;
and when the quality of the first sensor data does not meet the requirement, generating a second prompt message for indicating to reacquire the original sensor data, and sending the second prompt message to the terminal.
Preferably, the point cloud data is a first data set corresponding to a first sampling frequency, each data in the first data set has a first timestamp, and when the first timestamp is inconsistent with the first sampling frequency, the quality of the point cloud data is determined to be inconsistent with the requirement; and/or the number of the groups of groups,
the inertial navigation data is a second data set corresponding to a second sampling frequency, each data in the second data set is provided with a second time stamp, and when the second time stamp is inconsistent with the second sampling frequency, the quality of the inertial navigation data is determined to be inconsistent with the requirement; and/or the number of the groups of groups,
the wheel speed meter data are third data sets corresponding to third sampling frequency, each data in the third data sets is provided with a third time stamp, and when the third time stamp is inconsistent with the third sampling frequency, the quality of the wheel speed meter data is determined to be inconsistent with the requirement; and/or the number of the groups of groups,
the GPS positioning data is a fourth data set corresponding to a fourth sampling frequency, each data in the fourth data set is provided with a fourth time stamp, and when the fourth time stamp is inconsistent with the fourth sampling frequency, the quality of the GPS positioning data is determined to be inconsistent with the requirement; and/or the number of the groups of groups,
the image data is a fifth data set corresponding to a fifth sampling frequency, each data in the fifth data set is provided with a fifth time stamp, and when the fifth time stamp is inconsistent with the fifth sampling frequency, the quality of the image data is determined to be inconsistent with the requirement.
Preferably, the second sensor data is obtained according to the point cloud data, the inertial navigation data, the wheel speed meter data, the GPS positioning data and the image data;
the inertial navigation data comprises acceleration information, angular velocity information and attitude angle information of the vehicle;
the wheel speed data comprises angular velocity information, linear velocity information and vehicle yaw rate information of left and right wheels of the vehicle;
and carrying out fusion processing on the point cloud data, the acceleration information, the angular velocity information and the attitude angle information of the vehicle, the angular velocity information, the linear velocity information and the vehicle yaw rate information of the left and right wheels of the vehicle, the GPS positioning data and the image data to obtain second sensor data.
In a second aspect, the present invention provides an apparatus comprising a memory for storing a program and a processor for performing the method of any of the first aspects.
In a third aspect, the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method according to any of the first aspects.
According to the high-precision point cloud map construction method provided by the embodiment of the invention, the cloud server automatically responds and triggers the point cloud map construction tasks, so that the construction of a plurality of point cloud map tasks can be realized at the same time, and the difficulty that a large map is difficult to process is overcome; when the first sensor data accords with the data qualification judging rule, the construction of the point cloud map is carried out, and the error in constructing the point cloud map is reduced through verification of the integrity, the precision, the distortion, the smoothness and the continuity of the point cloud map, so that the requirement of high precision of the construction of the point cloud map is met, and the quality of the point cloud map is improved.
Drawings
Fig. 1 is a flow chart of a high-precision point cloud map construction method according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
The high-precision point cloud map construction method provided by the embodiment of the invention is applied to a cloud server and is used for constructing a high-precision point cloud map.
The sensor data required for constructing the high-precision point cloud map is collected by an intelligent vehicle, wherein the intelligent vehicle can be understood as an unmanned automatic driving vehicle. The intelligent vehicle is equipped with various sensors, the sources of the point cloud map data are based on the sensors, including but not limited to cameras, lidar, global positioning system (Global Positioning System, GPS), inertial measurement units (Inertial measurement unit, IMU) and wheel speed meters. The plurality of cameras and the plurality of laser radars are respectively arranged around the intelligent vehicle; the GPS is arranged at the top of the vehicle; the inertial measurement unit can be arranged at a hidden position such as a chassis of the vehicle; the wheel speed meters can be two, and are respectively arranged on the left wheel and the right wheel of the vehicle.
Fig. 1 is a flow chart of a high-precision point cloud map construction method according to an embodiment of the present invention.
As shown in fig. 1, the method for constructing a high-precision point cloud map according to the first embodiment of the present invention includes the following steps:
step 101, receiving first sensor data sent by a terminal; the first sensor data comprises point cloud data, inertial navigation data, wheel speed meter data, GPS positioning data and image data;
specifically, the point cloud data is data obtained by the lidar, and the data of the lidar may be one or more, and when the number of the lidar is more, the point cloud data represents a set of point cloud data obtained by the lidar.
The inertial navigation data refer to current acceleration information, angular velocity information and attitude angle information of the vehicle, which are acquired by an Inertial Measurement Unit (IMU).
The wheel speed meter data is angular velocity information, linear velocity information, and vehicle yaw rate information of the left and right wheels of the vehicle acquired by the wheel speed meter.
The GPS positioning data is position information of the vehicle acquired by GPS. Because of errors in GPS positioning, the location information of the vehicle is not the current accurate location information of the vehicle. In a preferred embodiment, to reduce errors in the location information of the vehicle and the current location of the vehicle, the GPS is preferably a differential GPS and the intelligent vehicle obtains the location information of the vehicle detected by the differential GPS.
The image data is environmental data around the vehicle collected by the cameras, and when the number of cameras is plural, the image data represents a set of the environmental data collected by the plural cameras.
Before step 101, the intelligent vehicle receives the driving information sent by the cloud server or the terminal, wherein the driving information comprises destination information and time information, and the intelligent vehicle automatically drives to a corresponding block according to the destination information and the time information. After receiving a map acquisition instruction sent by a terminal, a sensor arranged on the intelligent vehicle acquires data according to a preset route and a preset mode according to the map acquisition instruction; after the sensor collects data, the terminal indicates that the data collection is completed through a derived instruction. The map acquisition instruction can be input by a user through the terminal or sent by the server to the terminal.
The cloud server receiving the first sensor data sent by the terminal specifically includes:
receiving multi-frame original sensor data sent by a terminal; each frame of raw sensor data includes a frame header;
judging whether the transmission of the multi-frame original sensor data is finished or not according to the frame head;
and when the transmission is finished, splicing the original sensor data of the multiple frames according to the frame head to obtain first sensor data.
Because the types of the sensors are different, the input sensor data types and formats are different, so that the universality of the algorithm is not facilitated, the original sensor data is required to be preprocessed, the sensor data is converted into specific data types and formats, and the consistency of the data at the input end of the algorithm is realized. Therefore, the first sensor data is uploaded to the cloud server after the original sensor data is finished.
102, after receiving first sensor data, generating a map task construction request message for constructing a map;
specifically, the cloud server detects whether the first sensor data is uploaded in real time, and monitors whether the first sensor data is uploaded. When the uploading of the first sensor data is finished, a map construction command is triggered, the cloud server automatically distributes a task name with a unique identifier for a task corresponding to the map construction command, and different map task construction request messages are automatically generated according to different task names, so that the map is constructed, and the point cloud map construction task can be automatically responded and triggered through the cloud server.
104, judging whether the first sensor data accords with a data qualification judging rule when the current memory occupation amount is smaller than a preset memory occupation amount and the current map task construction number is smaller than a preset map task construction threshold;
specifically, the cloud server sets a plurality of configuration parameters, including the memory occupation amount and the task number.
The preset memory footprint may be understood as a memory footprint allowed by the server. In a specific example, the predetermined memory footprint may be set to 80%. The preset map task construction threshold may be understood as the number of tasks that can be run simultaneously on the cloud server, and in another specific example, the preset map task construction threshold is N,0< N <6, and N is a positive integer.
The cloud server can judge whether the point cloud map construction task needs to wait for other tasks to finish by judging the occupied amount of the memory and the number of the tasks running simultaneously, and if so, the point cloud map construction task is executed until the other tasks are finished. In a specific example, when the memory occupation amount is 60%, the cloud server can execute the point cloud map construction task without waiting when the number of tasks running on the cloud server simultaneously is 4. Therefore, the cloud point map construction is carried out through the cloud server, the defect that the vehicle-mounted processor is high in requirement when the map construction is carried out through the vehicle-mounted processor in the existing online map construction technology can be avoided, the construction of a plurality of cloud point map tasks can be simultaneously realized, and the difficulty that a large map is difficult to process is overcome.
specifically, the data qualification judging rule specifically includes whether the format of the first sensor data is a preset format and whether the quality of the first sensor data meets the requirements.
When the format of the first sensor data does not accord with the preset format, the cloud server generates a first prompt message and sends the first prompt message to the terminal so as to instruct the terminal to acquire the first sensor data again, namely, the original sensor data is rearranged and uploaded to the cloud server according to the preset format.
Furthermore, since each sensor collects data according to the preset sampling frequency, whether the quality of the first sensor data meets the requirement is judged by checking whether the data measured by a certain sensor is missing in the first sensor data.
The point cloud data are first data sets corresponding to the first sampling frequency, each data in the first data sets is provided with a first time stamp, and when the first time stamp is inconsistent with the first sampling frequency, the quality of the point cloud data is determined to be inconsistent with the requirement; and/or the inertial navigation data is a second data set corresponding to the second sampling frequency, each data in the second data set is provided with a second time stamp, and when the second time stamp is inconsistent with the second sampling frequency, the quality of the inertial navigation data is determined to be inconsistent with the requirement; and/or the wheel speed meter data is a third data set corresponding to a third sampling frequency, each data in the third data set is provided with a third time stamp, and when the third time stamp is inconsistent with the third sampling frequency, the quality of the wheel speed meter data is determined to be inconsistent with the requirement; and/or the GPS positioning data is a fourth data set corresponding to a fourth sampling frequency, each data in the fourth data set is provided with a fourth time stamp, and when the fourth time stamp is inconsistent with the fourth sampling frequency, the quality of the GPS positioning data is determined to be inconsistent with the requirement; and/or the image data is a fifth data set corresponding to the fifth sampling frequency, each data in the fifth data set is provided with a fifth time stamp, and when the fifth time stamp is inconsistent with the fifth sampling frequency, the quality of the image data is determined to be inconsistent with the requirement.
When the data quality of the first sensor does not meet the requirements, the cloud server generates a second prompt message and sends the second prompt message to the terminal so as to instruct the terminal to acquire the original sensor data again.
Therefore, the cloud server can execute the point cloud map construction task only when the format of the first sensor data accords with the preset format and the quality of the first sensor data accords with the requirement. The data qualification judgment rule is set, so that the quality of sensor data for constructing the point cloud map is greatly improved, and the data error of the point cloud map construction is reduced, thereby improving the quality of the constructed point cloud map.
specifically, the point cloud data, acceleration information, angular velocity information and attitude angle information of the vehicle, angular velocity information, linear velocity information and yaw rate information of the vehicle, GPS positioning data and image data are fused, and second sensor data, namely data obtained by fusing the first sensor data, are obtained. The first positioning data are positioning pose information of each point in the point cloud map, which is calculated by a preset algorithm in the point cloud map, wherein the positioning pose information can comprise positioning information and a pose angle.
specifically, the cloud server performs quality verification on the successfully constructed point cloud map. In order to reduce errors of map data and improve the quality of the map data, the constructed point cloud map must meet certain accuracy conditions. Therefore, in the method, besides adopting two differential GPS measurement, a Real-time kinematic (RTK) carrier phase differential technology is combined to acquire second positioning data, when a sign bit of the RTK is normal, the first positioning data is compared with the second positioning data, whether the difference value between the first positioning data and the second positioning data accords with a preset threshold value is judged, if the difference value is not more than the preset threshold value, the precision of the point cloud map accords with a precision condition, and meanwhile, a centimeter-level positioning result is given, so that the requirement of high-precision point cloud map construction is met.
specifically, inertial navigation data and wheel speed meter data are aligned in time according to the time stamp, and a certain preset length is selected to generate a local path. In a specific example, the preset length may be 10m. If the two aligned local paths (curves) are too different, it is indicated that the two curves are different in shape, and if they are in the same coordinate system, it is indicated that one of them is distorted.
specifically, smoothness refers to that a connecting line of points corresponding to a plurality of first positioning data in a constructed point cloud map is a smooth curve, if a certain point is jumped, a saw-tooth connecting line appears, and therefore whether the point cloud map meets the smoothness condition can be judged by judging whether the connecting line among the plurality of first positioning data is jumped or not.
specifically, the distances between the points corresponding to any two adjacent first positioning data among the plurality of first positioning data in the point cloud map are all provided with a preset distance threshold, so that the distances between the adjacent points in the point cloud map are compared with the preset distance threshold, and when the distances are not greater than the preset distance threshold, the continuity of the points is considered to be good, and the point cloud map meets the requirement of continuity. In a specific example, the preset distance threshold is 1m, and if the distance between two points is 2m, it can be determined that the continuity of the point cloud map does not meet the condition.
specifically, the completeness condition refers to determining whether points in the point cloud map are complete, and the points include, but are not limited to, functional points, traffic light identification data and boundary information. The function points refer to preset points on the point cloud map. For example, in a point cloud map on a user terminal, the selected point. The traffic light identification data refers to the location of the traffic light, such as the coordinates of the traffic light on a point cloud map, which may be the location of the center point of the light panel frame.
In one example, when the boundary information is judged to be complete, the number of track points on the boundary can be calculated according to the frequency of the sensor and the acquisition time of the sensor, and whether the boundary track data in the generated point cloud map is consistent with the calculated number of track points on the boundary or not is checked. And when the boundary track data in the generated point cloud map is consistent with the calculated number of track points on the boundary, determining that the boundary information meets the complete condition. And when the functional point, the traffic light identification data and the boundary information all meet the complete condition, determining that the point cloud map meets the complete condition.
And step 112, determining that the point cloud map passes verification when the point cloud map meets the precision condition, the distortion condition, the smoothness condition, the continuity condition and the integrity condition.
In a preferred embodiment, when the verification of the point cloud map is failed, for example, the point cloud map does not meet the accuracy condition, the distortion condition, the smoothness condition and the continuity condition, the cloud server automatically modifies the designated parameters required for the point cloud map construction, for example, the positioning deviation, the positioning state and the like, and re-performs the point cloud map construction once with the new parameters, and then re-performs the quality verification of the point cloud map.
If the point cloud map does not meet the complete condition, the cloud server gives out a specific data missing item, and prompts the terminal to acquire and upload the original data again. Steps 101-112 are then re-performed until the point cloud map quality meets the integrity condition.
By verifying the point cloud map, errors in the process of constructing the point cloud map are reduced, and therefore the quality of the point cloud map is improved.
Further, the present application may further include: and after the verification of the point cloud map is passed, storing the sensor data corresponding to the point cloud map so as to splice the stored sensor data corresponding to the multiple areas later to obtain the point cloud map with a larger block.
According to the high-precision point cloud map construction method provided by the embodiment of the invention, the cloud server automatically responds and triggers the point cloud map construction, so that the construction of a plurality of point cloud map tasks can be realized at the same time, and the difficulty that a large map is difficult to process is overcome; when the first sensor data accords with the data qualification judging rule, the point cloud map is constructed, and the error of the constructed point cloud map is reduced through verification of the integrity, the precision, the distortion, the smoothness and the continuity of the point cloud map, so that the requirement of high precision of the point cloud map construction is met, and the quality of the point cloud map is improved.
The second embodiment of the invention provides a device, which comprises a memory and a processor, wherein the memory is used for storing a program, and the processor is used for executing the method provided by the first embodiment of the invention.
The third embodiment of the present invention provides a computer readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method provided in the first embodiment of the present invention is implemented.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of function in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be placed in random access memory (RA high precision point cloud map construction method), memory, read only memory (RO high precision point cloud map construction method), electrically programmable RO high precision point cloud map construction method, electrically erasable programmable RO high precision point cloud map construction method, registers, hard disk, removable disk, CD-RO high precision point cloud map construction method power system control method, or any other form of storage medium known in the art.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (9)
1. The high-precision point cloud map construction method is characterized by comprising the following steps of:
receiving first sensor data sent by a terminal; the first sensor data comprises point cloud data, inertial navigation data, wheel speed meter data, GPS positioning data and image data;
generating a map task construction request message for constructing a map after receiving the first sensor data;
acquiring the current memory occupation amount and the current map task construction number according to the map task construction request message;
when the current memory occupation amount is smaller than the preset memory occupation amount and the current map task construction number is smaller than a preset map task construction threshold value, judging whether the first sensor data accords with a data qualification judging rule or not;
when the first sensor data accords with the data qualification judging rule, obtaining second sensor data according to the point cloud data, the inertial navigation data, the wheel speed meter data, the GPS positioning data and the image data;
constructing a point cloud map according to the second sensor data; the point cloud map includes a plurality of points, each of the points having first positioning data;
verifying the point cloud map, when a flag bit of a dynamic carrier phase difference technology RTK represents that a signal is normal, comparing the first positioning data with second positioning data obtained through a real-time dynamic carrier phase difference technology RTK, and when the difference value between the first positioning data and the second positioning data is not greater than a preset threshold value, determining that the point cloud map meets an accuracy condition;
generating a local path with preset length according to the inertial navigation data and the wheel speed meter data, comparing the local path with local paths in a point cloud map with the same preset length, and determining that the point cloud map meets distortion conditions when the difference value of the curvatures of the points of the local path and the local path in the point cloud map with the same preset length is not larger than a preset difference value threshold;
judging whether jump exists in the connecting lines among a plurality of first positioning data in the point cloud map, and determining that the point cloud map meets the smoothness condition when no jump exists;
judging whether the distance between any two adjacent points corresponding to the first positioning data in the point cloud map is not more than a preset distance threshold value, and determining that the point cloud map meets a continuity condition when the distance is not more than the preset distance threshold value;
judging whether the point is complete, and when the point is complete, determining that the point cloud map meets the complete condition;
and when the point cloud map accords with the precision condition, the distortion condition, the smoothness condition, the continuity condition and the integrity condition, determining that the point cloud map passes verification.
2. The method for constructing a high-precision point cloud map according to claim 1, wherein the first sensor data sent by the receiving terminal specifically includes:
receiving multi-frame original sensor data sent by a terminal; each frame of the original sensor data comprises a frame header;
judging whether the transmission of the multi-frame original sensor data is finished or not according to the frame header;
and when the transmission is finished, splicing the original sensor data of the multiple frames according to the frame head to obtain first sensor data.
3. The method for constructing a high-precision point cloud map according to claim 1, wherein the step of receiving the first sensor data transmitted by the terminal includes:
the sensor receives a map acquisition instruction sent by a terminal and acquires data according to a preset route and a preset mode according to the map acquisition instruction;
after the data acquisition is completed, the terminal indicates the data acquisition is completed through the export instruction.
4. The method of claim 1, wherein determining whether the first sensor data meets the data qualification determination rule specifically comprises:
judging whether the format of the first sensor data is a preset format or not;
and when the format of the first sensor data does not accord with the preset format, generating a first prompt message for indicating to reacquire the first sensor data, and sending the first prompt message to a terminal.
5. The method of claim 1, wherein determining whether the first sensor data meets the data qualification determination rule specifically comprises:
judging whether the data quality of the first sensor meets the requirement or not;
and when the quality of the first sensor data does not meet the requirement, generating a second prompt message for indicating to reacquire the original sensor data, and sending the second prompt message to the terminal.
6. The method of claim 1, wherein the point cloud data is a first data set corresponding to a first sampling frequency, each data in the first data set having a first timestamp, and determining that the point cloud data quality is unsatisfactory when the first timestamp is inconsistent with the first sampling frequency; and/or the number of the groups of groups,
the inertial navigation data is a second data set corresponding to a second sampling frequency, each data in the second data set is provided with a second time stamp, and when the second time stamp is inconsistent with the second sampling frequency, the quality of the inertial navigation data is determined to be inconsistent with the requirement; and/or the number of the groups of groups,
the wheel speed meter data are third data sets corresponding to third sampling frequency, each data in the third data sets is provided with a third time stamp, and when the third time stamp is inconsistent with the third sampling frequency, the quality of the wheel speed meter data is determined to be inconsistent with the requirement; and/or the number of the groups of groups,
the GPS positioning data is a fourth data set corresponding to a fourth sampling frequency, each data in the fourth data set is provided with a fourth time stamp, and when the fourth time stamp is inconsistent with the fourth sampling frequency, the quality of the GPS positioning data is determined to be inconsistent with the requirement; and/or the number of the groups of groups,
the image data is a fifth data set corresponding to a fifth sampling frequency, each data in the fifth data set is provided with a fifth time stamp, and when the fifth time stamp is inconsistent with the fifth sampling frequency, the quality of the image data is determined to be inconsistent with the requirement.
7. The method for constructing a high-precision point cloud map according to claim 1, wherein the second sensor data is obtained according to the point cloud data, inertial navigation data, wheel speed meter data, GPS positioning data and image data;
the inertial navigation data comprises acceleration information, angular velocity information and attitude angle information of the vehicle;
the wheel speed data comprises angular velocity information, linear velocity information and vehicle yaw rate information of left and right wheels of the vehicle;
and carrying out fusion processing on the point cloud data, the acceleration information, the angular velocity information and the attitude angle information of the vehicle, the angular velocity information, the linear velocity information and the vehicle yaw rate information of the left and right wheels of the vehicle, the GPS positioning data and the image data to obtain second sensor data.
8. An apparatus comprising a memory for storing a program and a processor for performing the method of any of claims 1-7.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the method according to any of claims 1-7.
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