CN115711623A - Positioning optimization method, system, equipment and storage medium based on grid map - Google Patents

Abstract

The invention provides a positioning optimization method, system, device and storage medium based on a grid map. The method includes the following steps: gridding the target area; collecting at least two positioning evaluations based on at least one vehicle passing through each grid The reference value corresponding to the method; the grid positioning performance value is obtained based on each reference value of each grid; for the grid whose positioning performance value is lower than the preset threshold, the positioning environment is modified for the grid. The invention can effectively evaluate the positioning performance of each subdivided area in the port environment, and adjust and reform the areas with poor positioning performance in a targeted manner.

Description

Location optimization method, system, device and storage medium based on grid map

technical field

The present invention relates to the technical field of navigation and positioning, in particular to a grid map-based positioning optimization method, system, device and storage medium.

Background technique

In order to achieve precise planning and control in the process of autonomous navigation, unmanned vehicles must have high positioning performance, while port unmanned trucks have higher requirements for positioning performance due to the particularity of the port operating environment. harsh. How to evaluate the positioning effect of the port driverless vehicle positioning algorithm in the actual scene, so as to carry out targeted optimization iterations on the algorithm, is an urgent problem to be solved.

Existing positioning evaluation methods generally use high-precision measuring equipment to measure the true value of the vehicle's pose, compare the estimated pose of the vehicle output by the positioning algorithm with the absolute true value measured by the measuring equipment, and evaluate the positioning based on the difference between the two. The precision of the algorithm. This method has a large workload and can only be evaluated from the positioning accuracy.

In view of this, the present invention provides a positioning optimization method, system, device and storage medium based on a grid map.

It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those of ordinary skill in the art.

Contents of the invention

In view of the problems in the prior art, the object of the present invention is to provide a positioning optimization method, system, equipment and storage medium based on grid maps, which overcomes the difficulties of the prior art and can effectively evaluate each segmented area in the port environment positioning performance, and make targeted adjustments to areas with poor positioning performance.

Embodiments of the present invention provide a grid map-based positioning optimization method, comprising the following steps:

Mesh the target area;

Collecting reference values corresponding to at least two positioning evaluation methods based on at least one vehicle passing through each grid;

obtaining said grid location performance values based on respective reference values for each of said grids; and

For the grid whose positioning performance value is lower than the preset threshold, the positioning environment modification is performed on the grid.

Preferably, said meshing the target area includes:

According to the preset grid size, the target area is divided into grids.

Preferably, the collection of reference values corresponding to at least two positioning evaluation methods based on at least one vehicle passing through each grid includes:

Collect logbooks of vehicles operating within the target area;

Taking each grid as a unit, at least collecting a satellite positioning divergence parameter, a positioning accuracy parameter, and a positioning robustness parameter when the vehicle passes through the grid.

Preferably, at least collecting the divergence parameters, positioning accuracy parameters, and positioning robustness parameters of the satellite positioning when the vehicle passes through the grid by taking each grid as a unit, including:

The satellite positioning includes a signal convergence state and a signal divergence state, and the percentage of the signal state divergence period to the total time is obtained as the satellite positioning divergence parameter.

Preferably, at least collecting the divergence parameters, positioning accuracy parameters, and positioning robustness parameters of the satellite positioning when the vehicle passes through the grid by taking each grid as a unit, including:

A first positioning result obtained when the satellite positioning is in a signal convergence state;

a second positioning result output by the positioning algorithm;

The positioning accuracy parameter is obtained based on an error between the first positioning result and the second positioning result.

Preferably, the error includes an error in the lateral information of the vehicle, an error in the longitudinal information of the vehicle, and an error in the heading angle between the first positioning result and the second positioning result.

Preferably, at least collecting the divergence parameters, positioning accuracy parameters, and positioning robustness parameters of the satellite positioning when the vehicle passes through the grid by taking each grid as a unit, including:

Performing polynomial fitting on the track points output by the positioning algorithm to obtain a curve, calculating the variance of the distance from the track point to the fitted curve, and obtaining the smoothness of positioning according to the variance;

Record the failure coordinates of the vehicle each time it fails due to a positioning problem, and obtain the failure rate according to the density of the failure coordinates in each grid;

Positioning robustness parameters of the grid are obtained based at least on smoothness and failure rate.

Preferably, the obtaining the grid positioning performance value based on each reference value of each grid includes:

The grid positioning performance value is obtained based on weighted calculation of each reference value of each grid.

Preferably, for the grid whose positioning performance value is lower than a preset threshold, performing positioning environment modification on the grid includes:

Obtaining the grid whose positioning performance value is lower than a preset threshold as a target grid;

Generate location identification coordinates based on the two sides of the road in the electronic map corresponding to the target grid, and each location identification coordinate has preset high-precision coordinates;

Adding an entity location identifier to the target area based on the coordinates of the location identifier.

Embodiments of the present invention also provide a positioning optimization system based on a grid map, which is used to implement the above-mentioned positioning optimization method based on a grid map. The positioning optimization system based on a grid map includes:

Grid planning module to grid the target area;

A positioning evaluation module, collecting reference values corresponding to at least two positioning evaluation methods when at least one vehicle passes through each grid;

A weighted calculation module, based on the weighted calculation of each reference value of each grid, to obtain the grid positioning performance value; and

The environment modification module is configured to modify the positioning environment of the grids whose positioning performance values are lower than a preset threshold.

Embodiments of the present invention also provide a positioning optimization device based on a grid map, including:

processor;

a memory storing executable instructions for the processor;

Wherein, the processor is configured to execute the steps of the above grid map-based positioning optimization method by executing executable instructions.

An embodiment of the present invention also provides a computer-readable storage medium for storing a program, and when the program is executed, the steps of the above grid map-based positioning optimization method are implemented.

The positioning optimization method, system, equipment and storage medium based on the grid map of the present invention can effectively evaluate the positioning performance of each subdivided area in the port environment, and carry out targeted adjustment and reconstruction of areas with poor positioning performance .

Description of drawings

Other characteristics, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments with reference to the following drawings.

FIG. 1 is a flow chart of the grid map-based positioning optimization method of the present invention.

FIG. 2 is a schematic diagram of the steps of an implementation process of the grid map-based positioning optimization method of the present invention.

3 to 7 are schematic diagrams of scenarios of an implementation process of the grid map-based positioning optimization method of the present invention.

FIG. 8 is a schematic structural diagram of the grid map-based positioning optimization system of the present invention.

FIG. 9 is a schematic structural diagram of a grid map-based positioning optimization device according to the present invention. as well as

FIG. 10 is a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present invention.

Detailed ways

Embodiments of the present application are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in the present application. The present application can also be implemented or applied in different specific implementation modes, and the details in the present application can also be modified or changed according to different viewpoints and application systems without departing from the spirit of the present application. It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

The embodiments of the present application will be described in detail below with reference to the accompanying drawings, so that those skilled in the art of the present application can easily implement them. The present application can be embodied in various forms, and is not limited to the embodiments described here.

In the expressions of this application, references to the terms "one embodiment," "some embodiments," "example," "specific examples," or "some examples," etc., mean specific features represented in connection with the embodiment or example. , structure, material or characteristic is included in at least one embodiment or example of the present application. Furthermore, the specific features, structures, materials or characteristics represented may be combined in any suitable manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and combine different embodiments or examples and features of different embodiments or examples presented in the present application without conflicting with each other.

In addition, the terms "first" and "second" are only used for expressive purposes, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include at least one of these features. In the expressions of this application, "plurality" means two or more, unless otherwise specifically defined.

In order to clarify the present application, components irrelevant to the description are omitted, and the same or similar components are given the same reference signs throughout the specification.

Throughout the specification, when it is said that a device is "connected" to another device, this includes not only the case of "direct connection" but also the case of "indirect connection" with other elements interposed therebetween. In addition, when it is said that a certain device "includes" a certain component, unless there is a specific statement to the contrary, it does not exclude other components, but means that other components may also be included.

When an element is said to be "on" another element, it can be directly on the other element, but it can also be with other elements in between. When a reference is made to say that an element is "directly on" another element, the other elements are not intervening.

Although the terms first, second, etc. are used in the present invention to denote various elements in some instances, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first interface, a second interface, and the like are indicated. Furthermore, as used in this invention, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It should be further understood that the terms "comprising", "comprising" indicate the presence of features, steps, operations, elements, components, items, species, and/or groups, but do not exclude one or more other features, steps, operations, elements, The existence, occurrence, or addition of components, items, categories, and/or groups. The terms "or" and "and/or" as used herein are to be construed as inclusive, or to mean either one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: A; B; C; A and B; A and C; B and C; A, B and C" . Exceptions to this definition will only arise when combinations of elements, functions, steps or operations are inherently mutually exclusive in some way.

The terminology used herein is only used to refer to specific embodiments, and is not intended to limit the application. The singular form used here also includes the plural form as long as the statement does not clearly indicate the opposite meaning. The meaning of "comprising" used in the description is to specify specific characteristics, regions, integers, steps, operations, elements and/or components, not to exclude other characteristics, regions, integers, steps, operations, elements and/or components exists or is attached.

Although not differently defined, all terms including technical terms and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms defined in commonly used dictionaries are additionally interpreted as having meanings consistent with the contents of relevant technical documents and current prompts, and as long as they are not defined, they should not be excessively interpreted as ideal or very formulaic meanings.

FIG. 1 is a flow chart of the grid map-based positioning optimization method of the present invention. As shown in Figure 1, the embodiment of the present invention provides a kind of location optimization method based on grid map, comprises the following steps:

S110. Perform gridding on the target area.

S120. Collect reference values corresponding to at least two positioning evaluation methods based on at least one vehicle passing through each grid.

S130. Obtain a grid positioning performance value based on each reference value of each grid. as well as

S140. For grids whose positioning performance value is lower than a preset threshold, modify the positioning environment of the grids.

The work flow of a method for comprehensively evaluating the positioning performance of unmanned trucks in ports proposed by the present invention, the method can analyze the positioning performance of multiple evaluation indicators in each subdivision area in a closed scene, and will The results of the analysis are visualized. This method can not only obtain quantitative data of positioning performance, but also directly observe the distribution of positioning performance in the global map. According to the analysis results, the environment of areas with weak positioning performance can be modified in a targeted manner, or the algorithm of a certain positioning index can be optimized to improve the overall performance of the positioning algorithm in the closed environment of the port.

In a preferred embodiment, step S110 includes:

According to the preset grid size, the target area is divided into grids, but not limited thereto.

In a preferred embodiment, step S120 includes:

S121. Collect driving logs of vehicles running in the target area.

S122. Taking each grid as a unit, at least collect a satellite positioning divergence parameter, a positioning accuracy parameter, and a positioning robustness parameter when the vehicle passes through the grid, but not limited thereto.

In a preferred embodiment, step S122 includes:

S1221. Satellite positioning includes a signal convergence state and a signal divergence state, and the percentage of the signal state divergence period to the total time is obtained as a satellite positioning divergence parameter, but not limited thereto.

In a preferred embodiment, step S122 includes:

S1222. A first positioning result obtained when the satellite positioning is in a signal convergence state.

S1223. A second positioning result output by the positioning algorithm.

S1224. Obtain a positioning accuracy parameter based on the error between the first positioning result and the second positioning result, but not limited thereto.

In a preferred embodiment, the error includes the error of the lateral information of the vehicle, the longitudinal information of the vehicle and the error of the heading angle between the first positioning result and the second positioning result, but is not limited thereto.

In a preferred embodiment, step S122 includes:

S1225. Perform polynomial fitting on the trajectory points output by the positioning algorithm to obtain a curve, calculate the variance of the distance from the trajectory point to the fitted curve, and obtain the smoothness of positioning according to the variance.

S1226. Record the failure coordinates of the vehicle each time a failure occurs due to the positioning problem, and obtain the failure rate according to the density of the failure coordinates in each grid.

S1227. Obtain a positioning robustness parameter of the grid based at least on the smoothness and failure rate, but not limited thereto.

In a preferred embodiment, step S130 includes:

Based on the weighted calculation of each reference value of each grid, the grid positioning performance value is obtained, but not limited thereto.

In a preferred embodiment, step S140 includes:

S141. Obtain a grid whose positioning performance value is lower than a preset threshold as a target grid.

S142. Generate coordinates with positioning marks based on the two sides of the road in the electronic map corresponding to the target grid, and each coordinate of the positioning marks has preset high-precision coordinates.

S143. Add an entity positioning mark to the target area based on the coordinates of the positioning mark, but not limited thereto.

The method of the present invention can grid the scene map, and evaluate multiple indicators for each grid, including positioning accuracy, positioning robustness, and GNSS distribution, so as to evaluate the positioning performance of each subdivided area in the port environment. After the analysis report is obtained, it is possible to specifically improve a certain indicator in areas with poor positioning performance.

FIG. 2 is a schematic diagram of the steps of an implementation process of the grid map-based positioning optimization method of the present invention. 3 to 7 are schematic diagrams of scenarios of an implementation process of the grid map-based positioning optimization method of the present invention. Referring to FIG. 3 , firstly, the map of the entire space scene is divided into several grids 2 (see FIG. 3 ).

Referring to FIG. 4 , the positioning effects of the vehicles 21 , 22 , and 23 in each grid are analyzed on various indicators, and the positioning performance of the positioning algorithm in different areas is obtained. By analyzing the positioning accuracy, positioning robustness, and GNSS state distribution of the area in each grid, the positioning performance of the positioning algorithm on different indicators is obtained. First, start from the online operation of the positioning algorithm in the domain controller when the vehicle is in normal operation, and collect some key log data (log data) while the algorithm is running, such as positioning track points for each frame, GNSS status, vehicle positioning error information, etc. , and collect data sets that can be used for offline operation of the algorithm. After the data set is downloaded to the local, the algorithm can be run offline locally, and logs can also be collected for analysis when running offline. Among them, GNSS generally refers to the global navigation satellite system (a space-based radio navigation and positioning system that provides information to users). User clock difference. GNSS is a space-based radio navigation and positioning system that can provide users with all-weather 3-dimensional coordinates, velocity and time information at any point on the earth's surface or in near-earth space. Therefore, in layman's terms, if you want to know the altitude in addition to the latitude and longitude, then you must receive 4 satellites to accurately locate. Positioning robustness includes positioning stability robustness (ie: positioning smoothness) and positioning quality robustness (ie: positioning failure rate). Whether a control system is robust is the key to its real practical application. Therefore, the design of modern control systems has taken robustness as one of the most important design indicators.

Referring to Figure 5, an evaluation standard for the strength of positioning performance is proposed, and the positioning performance is quantified and visualized, which can display the quantified values of various indicators of positioning performance in each grid in the map, and can use different The color indicates the strength of each grid's positioning performance, and you can more intuitively see the distribution of positioning performance in different areas in the scene. By constructing a data recovery system, it is ensured that the positioning algorithm evaluation method of the present invention has the capability of analyzing data generated by online operation and offline operation. In this embodiment, after the log data is obtained, it is mainly analyzed from three aspects: GNSS status (satellite positioning divergence parameters), positioning accuracy, and positioning robustness (positioning smoothness and positioning failure rate). There are many large-scale mechanical equipment in the port, and the GNSS signal is easily blocked. The ratio of the time when the GNSS state diverges to the total time is counted. At the same time, the quality of the GNSS state in the global map is represented by different colors to visualize the GNSS state. Distribution. The positioning result when the GNSS signal is good is taken as the true value, and the true value and the positioning result output by the positioning algorithm are compared with the lateral, longitudinal and heading angle errors of the vehicle to evaluate the accuracy of the positioning algorithm. The trajectory points output by the positioning algorithm are polynomially fitted, and the variance of the distance between the trajectory points and the fitted curve is calculated, and this variance is used as the smoothness of positioning. When the vehicle is working, record the coordinates of the vehicle every time it fails due to positioning problems, so as to calculate the failure rate of the vehicle in each subdivision area. In this embodiment, (1) satellite positioning includes a signal convergence state and a signal divergence state, and the percentage of the signal state divergence time period to the total duration is obtained as a satellite positioning divergence parameter. (2) By using the first positioning result obtained when the satellite positioning is in a signal convergence state. The second positioning result output by the positioning algorithm. A positioning accuracy parameter is obtained based on an error between the first positioning result and the second positioning result. The error includes the error of the vehicle lateral information, the error of the vehicle longitudinal information and the error of the heading angle between the first positioning result and the second positioning result. (3) Perform polynomial fitting on the trajectory points output by the positioning algorithm to obtain a curve, calculate the variance of the distance from the trajectory point to the fitted curve, and obtain the smoothness of positioning according to the variance. Record the failure coordinates of the vehicle every time it fails due to the positioning problem, and obtain the failure rate according to the density of the failure coordinates in each grid. Positioning robustness parameters of the grid are obtained based at least on smoothness and failure rate.

Then, after calculating the data of each index, rasterize the global map, calculate the value of each index in each grid, assign different weights to each index, and calculate the positioning performance of each grid. value. For example: Assign a different color to each raster according to the value of its positioning performance. Thereby, the quantitative value and the distribution map of the positioning performance are obtained.

In this embodiment, the first area distributed by the point grid 31 in the map satisfies the preset positioning performance threshold (greater than or equal to the preset positioning performance threshold), and the distribution of the oblique grid 32 (due to signal occlusion by buildings) The second area does not meet the preset positioning performance threshold (less than the preset positioning performance threshold). Positioning identification coordinates 41 are generated on both sides of the road in the electronic map corresponding to the oblique grid 32 , and each positioning identification coordinate 41 has preset high-precision coordinates. In the future, this result can be comprehensively analyzed and evaluated to provide algorithm developers with directions for algorithm optimization or environment modification (such as increasing the number of fixed road signs in the environment). Thereby improving the overall algorithm performance of port driverless cars. For example: in this example, the entity positioning mark 42 is added to the space scene based on the positioning mark coordinates 41, but it is not limited thereto. In a preferred solution, the positioning mark coordinates 41 can be the center of the diamond-shaped block mark, and the entity positioning mark 42 is a highly reflective diamond-shaped block pattern. Point b, obtain the third point c and the fourth point d located at both ends along the length of the road; establish the first connection line based on the first point a and the second point b, and establish the second point based on the third point c and the fourth point d For the connection line, the intersection point e of the first connection line and the second connection line is used as the centroid of the point cloud subset, and the location information of the centroid corresponds to the position of the location identification coordinate 41 in the electronic map.

The subsequent process of using the entity positioning marker 42 includes: during the driving process of the vehicle, continuously detect obstacles in the environment through the laser radar, and obtain relevant point cloud information. According to the high reflectivity feature of the highly reflective rhombic block pattern, the points with high reflectivity are extracted in the point cloud containing ground information. The point cloud information is obtained by collecting the laser reflection points on the road surface with the laser radar. The point cloud information includes coordinate information and reflection intensity information in the laser radar coordinate system, and after filtering the point cloud whose reflection intensity information is lower than the preset reflection threshold, Get point cloud collection.

After point cloud processing, after obtaining the highly reflective diamond-shaped block pattern information, it needs to be segmented more accurately to facilitate subsequent feature extraction. First, according to the reflection intensity of the point cloud, the local point cloud is roughly segmented, and the filtering operation is performed to remove outliers and noise points on the surface, so that the local point cloud is as regular and complete as possible. Based on the results of point cloud clustering in the point cloud collection, image segmentation is performed to obtain several point cloud subsets, thereby obtaining a point cloud subset representing a highly reflective diamond block pattern. A subset of the cloud is identified, resulting in a subset of the point cloud representing a highly reflective diamond block pattern. It is also possible to directly perform point cloud recognition on the point cloud collection, and segment the point cloud subsets representing each highly reflective diamond block pattern.

Perform feature extraction, and extract the centroid of the point cloud as the center of the highly reflective diamond block pattern according to the point cloud processing result. The above steps realize the laser front-end function. For the point cloud subset, the first point a and the second point b located at both ends are obtained along the road width direction, and the third point c and the fourth point d located at both ends are obtained along the road length direction; based on the first point a and the second point b Establish the first connection line, establish the second connection line based on the third point c and the fourth point d, use the intersection point e of the first connection line and the second connection line as the centroid of the point cloud subset, and set the centroid corresponding to The coordinate information in the lidar coordinate system is projected to the car body coordinate system. Due to the particularity of the geometric structure of the highly reflective rhombus pattern, the positions of the four vertices can be easily obtained, and the position of the centroid can be obtained through the intersection point e of the lines connecting the vertices. Compared with the method of obtaining the centroid by fitting each circular mark, this greatly saves the amount of calculation.

Project each centroid to the car body coordinate system, and convert the point cloud information detected by the lidar to the car body coordinate system according to the external parameters from the lidar to the car body coordinate system. In closed-loop operation scenarios, such as docks, parks, etc., the characteristics of related objects in the operation site are usually collected as prior information. The high-reflection diamond block pattern positioning method proposed in this paper has its coordinate information included in the global map. Subsequent vehicles are positioned through the preset high-precision coordinate information corresponding to these highly reflective rhombic blocks to obtain more accurate positioning. Therefore, the second area (diagonal grid 32 ) that does not meet the preset positioning performance threshold is changed into an area that can also perform high-precision positioning, reducing positioning blind spots. The positioning optimization method based on the grid map of the present invention can effectively evaluate the positioning performance of each subdivided area in the port environment, and adjust and transform the areas with poor positioning performance in a targeted manner.

FIG. 7 is a schematic structural diagram of the grid map-based positioning optimization system of the present invention. As shown in Figure 7, the positioning optimization system 5 based on the grid map of the present invention includes:

The grid planning module 51 performs gridding on the target area.

The positioning evaluation module 52 collects reference values corresponding to at least two positioning evaluation methods when at least one vehicle passes through each grid.

The weighted calculation module 53 obtains the grid positioning performance value based on the weighted calculation of each reference value of each grid. as well as

The environment modification module 54 is configured to modify the positioning environment of the grids whose positioning performance value is lower than the preset threshold.

In a preferred embodiment, the grid planning module 51 is configured to perform grid division on the target area according to a preset grid size.

In a preferred embodiment, the location evaluation module 52 is configured to collect logbooks of vehicles operating within the target area. Taking each grid as a unit, at least collect the divergence parameter, the positioning accuracy parameter and the positioning robustness parameter of the satellite positioning when the vehicle passes through the grid.

In a preferred embodiment, the positioning assessment module 52 is further configured to include signal convergence status and signal divergence status for satellite positioning, and obtain the percentage of the signal status divergence period to the total duration as the satellite positioning divergence parameter.

In a preferred embodiment, the positioning evaluation module 52 is further configured to obtain the first positioning result when the satellite positioning is in a signal convergence state. The second positioning result output by the positioning algorithm. A positioning accuracy parameter is obtained based on an error between the first positioning result and the second positioning result.

In a preferred embodiment, the positioning evaluation module 52 is further configured such that the errors include errors in vehicle lateral information, vehicle longitudinal information, and heading angles between the first positioning result and the second positioning result.

In a preferred embodiment, the positioning evaluation module 52 is further configured to perform polynomial fitting on the trajectory points output by the positioning algorithm to obtain a curve, calculate the variance of the distance from the trajectory point to the fitted curve, and obtain the smoothness of positioning according to the variance . Record the failure coordinates of the vehicle every time it fails due to the positioning problem, and obtain the failure rate according to the density of the failure coordinates in each grid. Positioning robustness parameters of the grid are obtained based at least on smoothness and failure rate.

In a preferred embodiment, the weighted calculation module 53 is configured to obtain the grid positioning performance value based on the weighted calculation of each reference value of each grid.

In a preferred embodiment, the environment modification module 54 is configured to obtain a grid whose positioning performance value is lower than a preset threshold as a target grid. Based on the two sides of the road in the electronic map corresponding to the target grid, coordinates with positioning marks are generated, and each coordinate of the positioning marks has preset high-precision coordinates. Add an entity positioning mark to the target area based on the positioning mark coordinates.

The positioning optimization system based on the grid map of the present invention can effectively evaluate the positioning performance of each subdivided area in the port environment, and adjust and transform the areas with poor positioning performance in a targeted manner.

An embodiment of the present invention also provides a grid map-based positioning optimization device, including a processor. Memory, in which are stored executable instructions for the processor. Wherein, the processor is configured as the steps of the grid map-based positioning optimization method performed by executing executable instructions.

As mentioned above, the positioning optimization device based on the grid map of the present invention can effectively evaluate the positioning performance of each subdivided area in the port environment, and adjust and transform the areas with poor positioning performance in a targeted manner.

Those skilled in the art can understand that various aspects of the present invention can be implemented as systems, methods or program products. Therefore, various aspects of the present invention can be embodied in the following forms, that is: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which can be collectively referred to herein as "circuit", "module", or "platform".

FIG. 8 is a schematic structural diagram of a grid map-based positioning optimization device according to the present invention. An electronic device 600 according to this embodiment of the present invention is described below with reference to FIG. 8 . The electronic device 600 shown in FIG. 8 is only an example, and should not limit the functions and scope of use of this embodiment of the present invention.

As shown in FIG. 8, electronic device 600 takes the form of a general-purpose computing device. Components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, and the like.

Wherein, the storage unit stores program codes, and the program codes can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present invention described in the above-mentioned electronic prescription circulation processing method part of this specification. For example, the processing unit 610 may perform the steps shown in FIG. 1 .

The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 6201 and/or a cache storage unit 6202 , and may further include a read-only storage unit (ROM) 6203 .

Storage unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, Implementations of networked environments may be included in each or some combination of these examples.

Bus 630 may represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local area using any of a variety of bus structures. bus.

The electronic device 600 can also communicate with one or more external devices 700 (such as keyboards, pointing devices, Bluetooth devices, etc.), and can also communicate with one or more devices that enable the user to interact with the electronic device 600, and/or communicate with Any device (eg, router, modem, etc.) that enables the electronic device 600 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interface 650 . Moreover, the electronic device 600 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and/or a public network such as the Internet) through the network adapter 660 . The network adapter 660 can communicate with other modules of the electronic device 600 through the bus 630 . It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives And data backup storage platform, etc.

An embodiment of the present invention also provides a computer-readable storage medium for storing a program, and the steps of the grid map-based positioning optimization method implemented when the program is executed. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on the terminal device, the program code is used to make the terminal device execute the above-mentioned The steps according to various exemplary embodiments of the present invention are described in the electronic prescription circulation processing method section.

As shown above, when the program of the computer-readable storage medium in this embodiment is executed, it can effectively evaluate the positioning performance of each subdivision area in the port environment, and adjust and transform the areas with poor positioning performance in a targeted manner. .

FIG. 9 is a schematic structural diagram of a computer-readable storage medium of the present invention. As shown in FIG. 9 , a program product 800 for implementing the above method according to an embodiment of the present invention is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program codes, and can be used in terminal equipment, For example running on a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program, and the program may be used by or in combination with an instruction execution system, apparatus or device.

A program product may take the form of any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: electrical connection with one or more conductors, portable disk, hard disk, random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

A computer readable storage medium may include a data signal carrying readable program code in baseband or as part of a carrier wave traveling as part of a data signal. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium other than a readable storage medium that can send, propagate or transport a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the readable storage medium may be transmitted by any suitable medium, including but not limited to wireless, cable, optical cable, RF, etc., or any suitable combination of the above.

Program codes for performing the operations of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., as well as conventional procedural programming Language - such as "C" or similar programming language. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server to execute. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider). business to connect via the Internet).

In summary, the grid map-based positioning optimization method, system, device, and storage medium of the present invention can effectively evaluate the positioning performance of each subdivided area in the port environment, and target areas with poor positioning performance. Make adjustments.

The above content is a further detailed description of the present invention in conjunction with specific preferred embodiments, and it cannot be assumed that the specific implementation of the present invention is limited to these descriptions. For those of ordinary skill in the technical field of the present invention, without departing from the concept of the present invention, some simple deduction or replacement can be made, which should be regarded as belonging to the protection scope of the present invention.

Claims (12)

1. A positioning optimization method based on a grid map is characterized by comprising the following steps:

gridding the target area;

collecting reference values corresponding to at least two positioning evaluation methods when at least one vehicle passes through each grid;

obtaining the grid positioning performance value based on each reference value of each grid; and

and carrying out positioning environment reconstruction on the grids with the positioning performance values lower than a preset threshold value.

2. The grid map-based positioning optimization method according to claim 1, wherein the gridding the target area comprises:

and carrying out gridding division on the target area according to the preset grid size.

3. The grid map-based positioning optimization method according to claim 1, wherein the collecting of the reference values corresponding to at least two positioning evaluation methods when at least one vehicle passes through each grid comprises:

collecting a driving log of a vehicle running in the target area;

and collecting at least a divergence parameter, a positioning precision parameter and a positioning robustness parameter of satellite positioning when the vehicle passes through the grids by taking each grid as a unit.

4. The grid-map-based positioning optimization method according to claim 3, wherein the collecting at least a divergence parameter, a positioning accuracy parameter and a positioning robustness parameter of satellite positioning of the vehicle passing through the grid in each grid unit comprises:

the satellite positioning comprises a signal convergence state and a signal divergence state, and the percentage of the time period of signal state divergence in the total time length is obtained as the satellite positioning divergence parameter.

5. The grid-map-based positioning optimization method according to claim 3, wherein the collecting at least a divergence parameter, a positioning accuracy parameter and a positioning robustness parameter of the satellite positioning of the vehicle passing through the grids in each grid unit comprises:

a first positioning result obtained when the satellite positioning is in a signal convergence state;

a second positioning result output by the positioning algorithm;

obtaining the positioning accuracy parameter based on an error between the first positioning result and the second positioning result.

6. The grid-map-based positioning optimization method according to claim 5, wherein the error comprises an error of vehicle lateral information, an error of vehicle longitudinal information and an error of heading angle between the first positioning result and the second positioning result.

7. The grid-map-based positioning optimization method according to claim 3, wherein the collecting at least a divergence parameter, a positioning accuracy parameter and a positioning robustness parameter of the satellite positioning of the vehicle passing through the grids in each grid unit comprises:

performing polynomial fitting on the track points output by the positioning algorithm to obtain a curve, calculating the variance of the distance from the track points to the fitted curve, and obtaining the positioning smoothness according to the variance;

recording a fault coordinate when the vehicle fails due to a positioning problem each time, and obtaining a fault rate according to the density of the fault coordinate in each grid;

a location robustness parameter for the grid is obtained based at least on smoothness and failure rate.

8. The grid map-based positioning optimization method according to claim 3, wherein the obtaining the grid positioning performance value based on the reference value of each grid comprises:

and obtaining the grid positioning performance value based on the weighted calculation of the reference values of each grid.

9. The grid map-based positioning optimization method according to claim 3, wherein the performing positioning environment modification on the grid with the positioning performance value lower than a preset threshold comprises:

obtaining the grids with the positioning performance values lower than a preset threshold value as target grids;

generating positioning identification coordinates on the basis of two sides of a road in an electronic map corresponding to the target grid, wherein each positioning identification coordinate has a preset high-precision coordinate;

and adding an entity positioning identifier to the target area based on the positioning identifier coordinates.

10. A grid map based location optimization system, the system comprising:

the grid planning module is used for gridding the target area;

the positioning evaluation module is used for collecting reference values corresponding to at least two positioning evaluation methods when at least one vehicle passes through each grid;

the weighting calculation module is used for obtaining the grid positioning performance value based on the weighting calculation of each reference value of each grid; and

and the environment reconstruction module is used for reconstructing the positioning environment of the grid with the positioning performance value lower than a preset threshold value.

11. A grid map-based positioning optimization device, comprising:

a processor;

a memory having stored therein executable instructions of the processor;

wherein the processor is configured to perform the steps of the grid map based positioning optimization method of any one of claims 1 to 9 via execution of the executable instructions.

12. A computer-readable storage medium storing a program, wherein the program is configured to implement the steps of the grid map-based positioning optimization method according to any one of claims 1 to 9 when executed.

Images