Intelligent laser positioning method, device, electronic device and storage medium

Abstract

The present application relates to an intelligent laser positioning method and device, and to the field of laser positioning technology. The method includes the following steps: based on the first laser point cloud information and GPS positioning information in the first environmental area, a grid map of the first environmental area is obtained; when the target vehicle is running in the first environmental area, the odometer information of the target vehicle and the second laser point cloud data in the second environmental area are obtained in real time; based on the first laser point cloud data and the second laser point cloud data, an updated grid map is obtained; based on the updated grid map and the odometer information of the target vehicle, the positioning trajectory of the target vehicle is obtained. The present application obtains the updated second laser point cloud data in the second environmental area within the first environmental area in real time, and then generates an updated grid map by fusing the updated second laser point cloud data with the GPS positioning information, so that the target vehicle can be positioned more accurately.

Description

Technical Field

[0001] The present application relates to the field of laser positioning technology and specifically to an intelligent laser positioning method, device, electronic equipment and storage medium. Background Art

[0002] With the continuous development and progress of artificial intelligence technology, self-driving cars with certain autonomous positioning and navigation capabilities have gradually been applied to home services, agricultural production, medical services, catering services, military and entertainment fields. At present, self-driving cars are increasingly being used for intelligent inspections in greenhouses, poultry farms and other places, as well as intelligent operations such as home sweeping and restaurant delivery. It can be seen that self-driving cars that can autonomously locate and navigate have important practical value and broad application prospects.

[0003] As an important branch of autonomous vehicle navigation research, the theoretical research of Simultaneous Localization and Mapping (SLAM) has developed rapidly and is a hot spot and difficulty in the field of autonomous vehicle research. In the existing technology, modern autonomous vehicles used in industrial parks usually need to pre-produce laser point cloud maps, and then locate the autonomous vehicle based on the laser point cloud maps.

[0004] Then, in actual application scenarios, the operating environment of the autonomous driving vehicle in the industrial park will usually change. At this time, using the pre-made laser point cloud map to locate the autonomous driving vehicle will cause positioning errors.

[0005] Therefore, how to improve the accuracy of full-scene laser positioning of autonomous driving vehicles is an urgent problem that needs to be solved. Summary of the invention

[0006] The present application provides an intelligent laser positioning method, device, electronic device and storage medium to improve the accuracy of full-scene laser positioning of autonomous driving vehicles.

[0007] To achieve the above objectives, this application provides the following solutions.

[0008] In a first aspect, the present application provides an intelligent laser positioning method, the method comprising the following steps:

[0009] Based on first laser point cloud information and GPS positioning information within the first environmental area, obtaining a grid map of the first environmental area;

[0010] When the target vehicle is running in the first environmental area, obtaining in real time the odometer information of the target vehicle and the second laser point cloud data in the second environmental area; wherein the second environmental area is located within the first environmental area;

[0011] Based on the first laser point cloud data and the second laser point cloud data, obtaining an updated grid map;

[0012] Based on the updated grid map and the odometer information of the target vehicle, a positioning track of the target vehicle is obtained.

[0013] Furthermore, the obtaining of the grid map of the first environmental area based on the first laser point cloud information and the GPS positioning information in the first environmental area comprises the following steps:

[0014] Matching a laser weight factor for the first laser point cloud information and a GPS weight factor for the GPS positioning information;

[0015] Based on the first weight factor and the second weight factor, the first laser point cloud information is integrated with the GPS positioning information to obtain positioning information within the first environmental area;

[0016] A grid map of the first environmental area is acquired based on the positioning information within the first environmental area.

[0017] Furthermore, matching the laser weight factor for the first laser point cloud information and matching the GPS weight factor for the GPS positioning information includes the following steps:

[0018] In an area where the GPS signal is poor within the first environmental area, setting the laser weight factor to be greater than the GPS weight factor;

[0019] In an area of ​​the first environmental area where the GPS signal is good, the laser weight factor is set to be smaller than the GPS weight factor.

[0020] Furthermore, when the target vehicle is running in the first environmental area, real-time acquisition of odometer information of the target vehicle includes the following steps:

[0021] When the target vehicle is running in the first environmental area, odometer information is transformed and generated based on the speed information and position information of the target vehicle at the current moment and the previous moment.

[0022] Furthermore, obtaining an updated grid map based on the first laser point cloud data and the second laser point cloud data comprises the following steps:

[0023] Replace the laser point cloud data corresponding to the second environment area in the first laser point cloud data with the second laser point cloud data to obtain updated laser point cloud data;

[0024] Based on the updated laser point cloud data and the GPS positioning information, an updated grid map is obtained.

[0025] Furthermore, the obtaining of the positioning track of the target vehicle based on the updated grid map and the odometer information of the target vehicle comprises the following steps:

[0026] A vehicle coordinate system is established with the target vehicle as the origin, and a position vector from the origin of the vehicle coordinate system to the origin of the updated grid map is used as a conversion vector;

[0027] Based on the conversion vector, the laser point cloud coordinates in the grid map coordinate system are converted into grid map information in the vehicle coordinate system;

[0028] Based on the odometer information of the target vehicle and the grid map information in the vehicle coordinate system, the positioning trajectory information of the target vehicle in the world coordinate system is converted.

[0029] Furthermore, the method further comprises the following steps:

[0030] When it is detected that the target vehicle enters the second environmental area multiple times, updating the second laser point cloud data within the second environmental area in real time;

[0031] Based on the updated second laser point cloud data, obtaining an updated second environmental area map;

[0032] Based on the updated second environment area map, a grid map is updated in real time.

[0033] In a second aspect, the present application provides an intelligent laser positioning device, the device comprising:

[0034] A map acquisition module, which is used to acquire a grid map of the first environmental area based on first laser point cloud information and GPS positioning information in the first environmental area;

[0035] A point cloud data acquisition module, which is used to acquire in real time the odometer information of the target vehicle and the second laser point cloud data in the second environmental area when the target vehicle is running in the first environmental area; wherein the second environmental area is located within the first environmental area;

[0036] An updating module, configured to obtain an updated grid map based on the first laser point cloud data and the second laser point cloud data;

[0037] A positioning trajectory acquisition module is used to acquire the positioning trajectory of the target vehicle based on the updated grid map and the odometer information of the target vehicle.

[0038] Furthermore, the map acquisition module includes:

[0039] A factor allocation submodule, which is used to match a laser weight factor for the first laser point cloud information and a GPS weight factor for the GPS positioning information;

[0040] a positioning information acquisition submodule, which is used to fuse the first laser point cloud information with the GPS positioning information based on the first weight factor and the second weight factor to obtain positioning information within the first environmental area;

[0041] The first map generating submodule is used to obtain a grid map of the first environmental area based on the positioning information in the first environmental area.

[0042] Furthermore, the factor allocation submodule includes:

[0043] A first allocation unit, configured to set the laser weight factor to be greater than the GPS weight factor in an area with poor GPS signals within the first environmental area;

[0044] The second allocation unit is used to set the laser weight factor to be smaller than the GPS weight factor in an area with good GPS signals in the first environmental area.

[0045] Furthermore, the point cloud data acquisition module is also used to transform and generate odometer information based on the speed information and posture information of the target vehicle at the current moment and the previous moment when the target vehicle is running in the first environmental area.

[0046] Furthermore, the update module includes:

[0047] a point cloud data updating submodule, which is used to replace the laser point cloud data corresponding to the second environment area in the first laser point cloud data with the second laser point cloud data to obtain updated laser point cloud data;

[0048] The second map generation submodule is used to obtain an updated grid map based on the updated laser point cloud data and the GPS positioning information.

[0049] Furthermore, the positioning trajectory acquisition module includes:

[0050] A conversion vector acquisition submodule, which is used to establish a vehicle coordinate system with the target vehicle as the origin, and use the position vector from the origin of the vehicle coordinate system to the origin of the updated grid map as the conversion vector;

[0051] A first information acquisition submodule, which is used to convert the laser point cloud coordinates in the grid map coordinate system into grid map information in the vehicle coordinate system based on the conversion vector;

[0052] The second information acquisition submodule is used to convert the odometer information of the target vehicle and the grid map information in the vehicle coordinate system into the positioning trajectory information of the target vehicle in the world coordinate system.

[0053] Furthermore, the intelligent laser positioning device also includes:

[0054] A first updating submodule, which is used to update the second laser point cloud data within the second environmental area in real time when it is detected that the target vehicle enters the second environmental area again;

[0055] A second updating submodule, which is used to obtain an updated second environmental area map based on the updated second laser point cloud data;

[0056] The third updating submodule is used to obtain an updated grid map based on the updated second environment area map.

[0057] The beneficial effects of the technical solution provided by this application include:

[0058] In the present application, the vehicle controller obtains a grid map of the first environmental area based on the first laser point cloud information and GPS positioning information in the first environmental area; when the target vehicle is running in the first environmental area, the odometer information of the target vehicle and the second laser point cloud data in the second environmental area are obtained in real time; wherein the second environmental area is located within the first environmental area; based on the first laser point cloud data and the second laser point cloud data, an updated grid map is obtained; based on the updated grid map and the odometer information of the target vehicle, the positioning trajectory of the target vehicle is obtained.

[0059] This application uses the fusion of laser point cloud data and GPS positioning information to locate the target vehicle, and obtains the updated second laser point cloud data in the second environmental area within the first environmental area in real time, and then uses the updated second laser point cloud data to fuse with the GPS positioning information to generate an updated raster map, so that the target vehicle can be located more accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 A flowchart of the steps of the intelligent laser positioning method provided in the embodiments of the present application;

[0062] Figure 2 This is a flowchart of the steps of the map acquisition method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0064] The embodiments of the present application are further described in detail below in conjunction with the accompanying drawings.

[0065] The embodiments of the present application provide an intelligent laser positioning method, device, electronic device and storage medium, which can more accurately locate a target vehicle.

[0066] In order to achieve the above technical effects, the overall idea of ​​this application is as follows:

[0067] See also Figure 1 As shown, an intelligent laser positioning method comprises the following steps:

[0068] S1. Based on first laser point cloud information and GPS positioning information within a first environmental area, obtaining a grid map of the first environmental area;

[0069] S2. When the target vehicle is running in the first environmental area, obtaining in real time the odometer information of the target vehicle and the second laser point cloud data in the second environmental area; wherein the second environmental area is located within the first environmental area;

[0070] S3. Acquire an updated grid map based on the first laser point cloud data and the second laser point cloud data;

[0071] S4. Based on the updated grid map and the odometer information of the target vehicle, obtain the positioning track of the target vehicle.

[0072] The embodiments of the present application are further described in detail below in conjunction with the accompanying drawings.

[0073] See also Figure 1 As shown, the embodiment of the present application provides an intelligent laser positioning method, which includes the following steps:

[0074] S1. Acquire a grid map of the first environmental area based on first laser point cloud information and GPS positioning information in the first environmental area.

[0075] The first environment area refers to the working area of ​​the target vehicle. For example, if the target driving vehicle is used to transport goods in an industrial park, the first environment area refers to all scenes in the industrial park, including roads, buildings, stacked objects, obstacles, etc.

[0076] Among them, the first laser point cloud information refers to the laser point cloud data used to characterize various scenes in the first environmental area obtained before the target vehicle starts working. The first laser point cloud information is obtained by using a laser radar transmitter installed on the target vehicle to emit a laser, and then using a laser receiver to receive the returned laser, thereby obtaining laser point cloud data corresponding to the scene.

[0077] Specifically, the target vehicle controller establishes a laser radar coordinate system with the geometric center of the laser radar as the origin, uses the laser radar to perform real-time scanning of the first environment area of ​​the target autonomous driving, accurately judges the distance and direction of the scene in the first environment area, obtains the first laser point cloud information required for the target vehicle navigation, and then fuses the obtained GPS positioning information of the target vehicle with the first laser point cloud information according to different weights.

[0078] A grid map of the first environmental area is drawn based on the positioning information obtained after fusion.

[0079] S2. When the target vehicle is running in the first environmental area, odometer information of the target vehicle and second laser point cloud data in the second environmental area are obtained in real time; wherein the second environmental area is located in the first environmental area.

[0080] The second environment area refers to an area in the first environment area where the GPS signal is poor. When the target vehicle drives into the second environment area of ​​the first environment area, the second laser point cloud data in the second environment area is acquired in real time, and the GPS positioning information of the target vehicle when driving in the second environment area is acquired in real time.

[0081] It should be noted that in the field of laser positioning, odometer is a method that uses data obtained from mobile sensors to estimate the change of an object's position over time. Odometer information can be divided into two types, one is the position and posture of the target vehicle, and the other is the speed of the target vehicle. In the field of laser navigation mapping and positioning, the kinematic information such as the linear velocity and angular velocity of the target vehicle and the mapping and positioning algorithm of laser navigation are used to estimate the position of the target vehicle.

[0082] Specifically, when the target vehicle is traveling in the first environmental area, the odometer information of the target vehicle is obtained in real time through the sensor installed on the target vehicle. When the target vehicle is detected to be running into the second environmental area where the GPS signal is poor in the first environmental area, the laser radar is turned on again to obtain real-time laser point cloud data. When the target vehicle is detected to be driving out of the second environmental area, the laser radar is turned off and the acquisition of real-time laser point cloud data is stopped.

[0083] S3, obtaining an updated grid map based on the first laser point cloud data and the second laser point cloud data;

[0084] Specifically, the target vehicle controller replaces the laser point cloud data corresponding to the second environment area in the first environment area with the second laser point cloud data corresponding to the second environment area to form updated first laser point cloud data corresponding to the first environment area, and then forms an updated raster map based on the updated first laser point cloud data.

[0085] S4. Based on the updated grid map and the odometer information of the target vehicle, the positioning trajectory of the target vehicle is obtained.

[0086] Specifically, the target vehicle controller establishes a target vehicle coordinate system with the center of the bottom of the target vehicle as the origin, and uses the position vector from the origin of the target vehicle coordinate system to the origin of the lidar coordinate system as the conversion vector, and converts the scene position coordinates corresponding to the first laser point cloud information in the lidar coordinate system into the scene position coordinates in the target vehicle coordinate system, and then combines the odometer information of the target vehicle to convert it into the positioning trajectory of the target vehicle in the world coordinate system.

[0087] In this embodiment, the target vehicle is located by fusing the laser point cloud data and the GPS positioning information, and the second laser point cloud data updated in the second environmental area within the first environmental area is obtained in real time. The updated second laser point cloud data is then fused with the GPS positioning information to generate an updated raster map, so that the target vehicle can be located more accurately.

[0088] In one embodiment of the application, Figure 2 As shown, step S1 includes:

[0089] S101, matching a laser weight factor for the first laser point cloud information and a GPS weight factor for the GPS positioning information;

[0090] Since using laser SLAM to obtain laser point cloud data will occupy a large amount of memory, thereby reducing the calculation efficiency of the target vehicle controller, the GPS positioning information can be integrated into the laser point cloud information to reduce the calculation amount of the target vehicle controller.

[0091] Specifically, the laser weight factor is matched for the first laser point cloud information, and the GPS weight factor is matched for the GPS positioning information; when the target vehicle enters an area with poor GPS signal in the first environmental area, the laser weight factor is set to be greater than the GPS weight factor; when the target vehicle drives into an area with good GPS signal in the first environmental area, the laser weight factor is set to be less than the GPS weight factor.

[0092] S102, based on the laser weight factor and the GPS weight factor, fusing the first laser point cloud information with the GPS positioning information to obtain positioning information in the first environmental area;

[0093] The target vehicle controller performs weighted averaging of the first laser point cloud information and the GPS positioning information according to the laser weight factor and the GPS weight factor to obtain positioning information within the first environmental area.

[0094] S103: Acquire a grid map of the first environmental area based on the positioning information in the first environmental area.

[0095] By using the open source Open SLAM Gmapping algorithm package and Rao-BlackWellized particle filtering algorithm under the ROS platform, the positioning information in the first environmental area in the world coordinate system is converted into a corresponding grid map to achieve the positioning and navigation of the target vehicle.

[0096] The embodiment of the present application assigns different weight factors to laser positioning information and GPS positioning. In the area with poor GPS signal in the first environmental area, the laser weight factor is set to be greater than the GPS weight factor; in the area with good GPS signal in the first environmental area, the laser weight factor is set to be less than the GPS weight factor, thereby establishing a more accurate raster map.

[0097] In one embodiment of the application, step S2 includes:

[0098] When the target vehicle is running in the first environment area, the odometer information is transformed and generated based on the speed information and position information of the target vehicle at the current moment and the previous moment.

[0099] The motor encoder sends the car's speed information to the target vehicle controller, and the inertial measurement unit IMU sends the car's posture information to the target vehicle controller. The starting point of the odometer is the origin of the target vehicle's motion. Combined with the encoder's return motor speed and the target vehicle's kinematic model, the target vehicle's speed information is obtained. By acquiring the target vehicle's position, angle, and speed information, the updated position coordinate information of the target vehicle relative to the origin of the world coordinate system is obtained.

[0100] In one embodiment of the application, step S2 includes:

[0101] Replace the laser point cloud data corresponding to the second environment area in the first laser point cloud data with the second laser point cloud data to obtain updated laser point cloud data;

[0102] It is understandable that areas with poor GPS signals are usually warehouses or workshops in industrial parks. In such areas with poor GPS signals, not only is the GPS signal poor, but the scenes in the warehouses or workshops are changing every day. Using pre-established point cloud information for positioning may cause positioning errors due to scene changes. Therefore, this part of the point cloud should be deleted, and real-time mapping and positioning should be used when entering the area with poor GPS signals for subsequent positioning.

[0103] Specifically, the target vehicle controller replaces the laser point cloud data corresponding to the second environmental area in the first laser point cloud data with the second laser point cloud data acquired in real time in the second environmental area to obtain updated laser point cloud data.

[0104] Based on the updated laser point cloud data and GPS positioning information, an updated raster map is obtained.

[0105] In this embodiment, laser point cloud data is acquired in real time in an area with poor GPS signals, that is, in the second environmental area, and the real-time acquired data is updated in the pre-acquired laser point cloud data of the overall environmental area to prevent inaccurate positioning of the target vehicle using pre-set laser point cloud data due to changes in the scene in the second environmental area.

[0106] In one embodiment of the application, step S4 includes:

[0107] S401, establishing a vehicle coordinate system with the target vehicle as the origin, and using the position vector from the origin of the target vehicle coordinate system to the origin of the updated grid map as a conversion vector;

[0108] Since the laser point cloud data obtained by the lidar uses the lidar coordinate system as a reference, the positioning information of the target vehicle needs to use the target vehicle coordinate system, that is, the world coordinate system, as the reference coordinate system. Therefore, the lidar coordinate system needs to be converted into the target vehicle coordinate system.

[0109] Specifically, a vehicle coordinate system is established with the target vehicle as the origin, and a position vector from the origin of the target vehicle coordinate system to the origin of the updated grid map is used as a conversion vector.

[0110] S402, based on the conversion vector, converting the laser point cloud coordinates in the grid map coordinate system into grid map information in the vehicle coordinate system;

[0111] According to the conversion vector, the laser point cloud coordinates are converted into raster map information in the vehicle coordinate system.

[0112] S403: Based on the odometer information of the target vehicle and the grid map information in the vehicle coordinate system, convert the information into the positioning trajectory information of the target vehicle in the world coordinate system.

[0113] In one embodiment, the method further includes: when it is detected that the target vehicle enters the second environmental area again, updating the second laser point cloud data in the second environmental area in real time; based on the updated second laser point cloud data, obtaining an updated second environmental area map; based on the updated second environmental area map, obtaining an updated grid map.

[0114] In this embodiment, as long as the target vehicle controller detects that the target leaves the second environment area and then re-enters the second environment area, it will re-map the second environment area in real time and replace the updated second laser point cloud data with the first laser point cloud data, thereby ensuring the accurate positioning of the target vehicle when the scene in the second environment area changes.

[0115] In one embodiment of the application, an intelligent laser positioning method is proposed, the method comprising the following steps:

[0116] A1, outside the area with poor GPS signal, select the starting point of laser SLAM according to the combined inertial navigation information, start the local laser SLAM process, and open the point cloud map established by laser SLAM through the point cloud display software. Because the laser SLAM accuracy is better and less offset during this period, the UTM coordinates of the four corner points of the rectangle of the GPS signal poor area can be obtained by selecting the coordinates of the points in the point cloud display software. This is the first process to obtain the four corner points, which can be used as a preliminary screening of the GPS signal poor area. The GPS signal poor area selected during screening should be 0.5 times larger than the actual GPS signal poor area (experience value, which can be adjusted according to actual conditions). The purpose of this is to prevent the map after global SLAM from offsetting with the current local map, resulting in a certain degree of change in the actual GPS signal poor area, which causes the actual GPS signal poor area to be not within the divided area. If there are multiple GPS signal poor areas, continue to divide according to the above method.

[0117] A2, when the starting point information of laser SLAM is assigned by GPS information, the starting point is changed to be expressed in the world coordinate system, so that laser SLAM changes from a local coordinate system to a global coordinate system. At the same time, when laser SLAM is subsequently performed, the GPS positioning information is added as a factor using factor graph optimization, and added to the factor graph optimization for multi-sensor fusion to improve positioning accuracy. When the program is running, each frame is judging whether the laser enters the GPS signal poor area. If it enters the GPS signal poor area, the GPS factor is not added. This ensures that the GPS positioning information provided by SLAM throughout the process is high-precision. Pure laser SLAM is used in the GPS signal poor area. In the industrial park, the GPS signal poor area is generally small, so the accuracy within time is guaranteed. Although the accuracy of laser SLAM is already high after integrating high-precision GPS information, it is still recommended to generate a "closed loop" in SLAM in practice, which can further improve the accuracy of laser SLAM and the consistency of the point cloud map of laser SLAM.

[0118] A3, when the whole laser SLAM map is completed, the accuracy of the laser SLAM point cloud map is already high. Reselect the GPS signal difference area in the established global laser SLAM point cloud map through the point cloud display software, and reduce the selected GPS signal difference area from 1.5 times the original GPS actual signal difference area to 1.2 times. Put each frame of laser positioning results of laser SLAM into the point cloud map of laser SLAM, and you can see the movement trajectory of the laser. By clicking each point in the point cloud display software, you can see the coordinate value xyz and the corresponding intensity of each point, as well as the posture information. In this program, the intensity of each point in the laser trajectory represents the id of each positioning point, and records the ids of all positioning points in the GPS signal difference area.

[0119] The laser positioning track in this program is in pcd format. The post-processing program converts the pcd format into txt format, and then deletes the positioning track in the GPS signal poor area recorded in the previous step in the txt text according to the ID. Then run the post-processing program to convert the txt text into a pcd file. The post-processing program will automatically fill in the disconnected ID in the middle while converting the format. The deleted ID in the middle is filled in by the undeleted ID later. The purpose of doing this is to ensure that the ID of the map track generated in the subsequent laser positioning is continuous, and the original laser positioning program framework can be changed as little as possible.

[0120] After deleting the id in the middle, the map id is discontinuous. The trajectory during map construction has been spliced ​​with the subsequent trajectory in the previous step. Therefore, the corresponding map file: Each key frame during laser SLAM map construction is saved in the pcd file format, and the file name corresponds to the trajectory id. Therefore, the pcd file corresponding to the id in the corresponding GPS signal difference area needs to be deleted in the post-processing program, and then the file name is spliced ​​as in the previous step, so that each pcd file name corresponds to the trajectory id in the newly generated pcd file in the previous step. In the post-processing program, output the first id to be renamed after the splicing of the latter part of the file, then output the number of the latter pcd files, and then execute the post-processing program, so that all the pcd files in the latter part can be renamed and correspond one by one with the id of the new trajectory pcd file in front.

[0121] A4, determine the current laser posture area and process the positioning information of the target vehicle:

[0122] Because in the workshop or warehouse, due to the daily cargo handling, the point cloud scene will change every day. Therefore, if the previously established point cloud is used for laser positioning, when the scene changes greatly, the laser positioning will be wrong. To address this problem, the present invention deletes the point cloud map of the area with poor GPS signal on this basis, starts the process of real-time key frame extraction and positioning before entering the area with poor GPS signal, and does not perform back-end optimization. The reason for this is that the laser point cloud features in the warehouse or workshop of the industrial park are more sufficient than those outdoors, so even if the back-end closed-loop optimization is not performed, the positioning accuracy in a small range is not a problem. At the same time, considering that the speed of vehicles in the industrial park is slow, each laser change is small, and the time consumption for matching with the local map iteration is short, and the real-time measurement is fast. The innovation of this is that in combination with the characteristics of the scene of the area with poor GPS signal in the point cloud of the industrial park, the targeted innovation is made by making full use of its poor GPS signal, but the point cloud features are rich and the features change every day. In this way, even if the point cloud in the warehouse or workshop changes every day, the laser positioning can still build and locate in its area in a stable and real-time manner.

[0123] If the target vehicle needs to enter and exit the GPS signal poor area for a long time and multiple times in the industrial park, if a local map is created each time it enters the GPS signal poor area, the memory will gradually decrease, and long-term operation may cause the memory to be full. And because the scene in the warehouse or workshop changes twice, for example, an object in the workshop has moved a distance compared to the last time it entered the workshop. If it moves multiple times, without releasing the local map of this area, there will be multiple ghosting interferences in the point cloud in the workshop later, thus affecting the accuracy of laser positioning. Therefore, each time after entering the GPS signal poor area, the id of each newly created local map will be recorded, and after leaving the GPS signal poor area, these key frames will be released: the point cloud feature information and corresponding posture corresponding to each id, and the local map in laser positioning will be rebuilt. In this way, it can be ensured that the laser can enter the GPS signal poor area for a long time, without limit on the number of times, and stable laser positioning.

[0124] In the embodiment of the present application, different SLAM mapping and positioning methods are intelligently performed according to the areas with poor GPS signals. At the same time, when the scene in the warehouse changes every day, a method for indoor laser positioning can be performed in real time according to the current scene to achieve high-robustness laser positioning in all scenes.

[0125] See also Figure 2 As shown, based on the same inventive concept as the real-time example of the intelligent laser positioning method, the embodiment of the present application provides an intelligent laser positioning device, which includes:

[0126] A map acquisition module, which is used to acquire a grid map of the first environmental area based on first laser point cloud information and GPS positioning information in the first environmental area;

[0127] A point cloud data acquisition module, which is used to acquire in real time the odometer information of the target vehicle and the second laser point cloud data in the second environmental area when the target vehicle is running in the first environmental area; wherein the second environmental area is located within the first environmental area;

[0128] An updating module, configured to obtain an updated grid map based on the first laser point cloud data and the second laser point cloud data;

[0129] A positioning trajectory acquisition module is used to acquire the positioning trajectory of the target vehicle based on the updated grid map and the odometer information of the target vehicle.

[0130] This application uses the fusion of laser point cloud data and GPS positioning information to locate the target vehicle, and obtains the updated second laser point cloud data in the second environmental area within the first environmental area in real time, and then uses the updated second laser point cloud data to fuse with the GPS positioning information to generate an updated raster map, so that the target vehicle can be located more accurately.

[0131] It should be noted that the intelligent laser positioning device provided in the embodiment of the present application, and its corresponding technical problems, technical means and technical effects are similar to the principles of the intelligent laser positioning method in terms of principle.

[0132] In a second aspect, an embodiment of the present application provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the intelligent laser positioning method mentioned in the first aspect is implemented.

[0133] In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program running on the processor, and when the processor executes the computer program, the intelligent laser positioning method mentioned in the first aspect is implemented.

[0134] It should be noted that, in this application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0135] The above is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. An intelligent laser positioning method, characterized in that: The method comprises the following steps: Based on first laser point cloud information and GPS positioning information within the first environmental area, obtaining a grid map of the first environmental area; When the target vehicle is running in the first environmental area, the odometer information of the target vehicle and the second laser point cloud data in the second environmental area are acquired in real time; wherein the second environmental area is located in the first environmental area, and the second environmental area is an area in the first environmental area where the GPS signal is poor; Based on the first laser point cloud information and the second laser point cloud data, obtaining an updated grid map; Based on the updated grid map and the odometer information of the target vehicle, obtaining the positioning track of the target vehicle; When the target vehicle is running in the first environmental area, real-time acquisition of odometer information of the target vehicle and second laser point cloud data in the second environmental area includes the following steps: When the target vehicle is traveling in the first environmental area, the odometer information of the target vehicle is obtained in real time through the sensor installed on the target vehicle. When it is detected that the target vehicle is running into the second environmental area where the GPS signal in the first environmental area is poor, the laser radar is turned on again to obtain real-time laser point cloud data. When it is detected that the target vehicle is driving out of the second environmental area, the laser radar is turned off and the acquisition of real-time laser point cloud data is stopped. The following steps are also included: When it is detected that the target vehicle enters the second environmental area again, updating the second laser point cloud data within the second environmental area in real time; Based on the updated second laser point cloud data, obtaining an updated second environmental area map; Based on the updated second environment area map, an updated grid map is obtained.

2. The intelligent laser positioning method according to claim 1, characterized in that: The step of obtaining a grid map of the first environmental area based on the first laser point cloud information and GPS positioning information in the first environmental area comprises the following steps: Matching a laser weight factor for the first laser point cloud information and a GPS weight factor for the GPS positioning information; Based on the laser weight factor and the GPS weight factor, the first laser point cloud information is integrated with the GPS positioning information to obtain positioning information within the first environmental area; A grid map of the first environmental area is acquired based on the positioning information within the first environmental area.

3. The intelligent laser positioning method according to claim 2, characterized in that: The matching of the laser weight factor for the first laser point cloud information and the matching of the GPS weight factor for the GPS positioning information include the following steps: In an area where the GPS signal is poor within the first environmental area, setting the laser weight factor to be greater than the GPS weight factor; In an area of ​​the first environmental area where the GPS signal is good, the laser weight factor is set to be smaller than the GPS weight factor.

4. The intelligent laser positioning method according to claim 1, characterized in that: When the target vehicle is running in the first environmental area, real-time acquisition of odometer information of the target vehicle includes the following steps: When the target vehicle is running in the first environmental area, odometer information is transformed and generated based on the speed information and position information of the target vehicle at the current moment and the previous moment.

5. The intelligent laser positioning method according to claim 1, characterized in that: The step of obtaining an updated grid map based on the first laser point cloud information and the second laser point cloud data comprises the following steps: Replace the laser point cloud data corresponding to the second environment area in the first laser point cloud information with the second laser point cloud data to obtain updated laser point cloud data; Based on the updated laser point cloud data and the GPS positioning information, an updated grid map is obtained.

6. The intelligent laser positioning method according to claim 1, characterized in that: The step of obtaining the positioning track of the target vehicle based on the updated grid map and the odometer information of the target vehicle comprises the following steps: Establishing a vehicle coordinate system with the target vehicle as the origin, and using a position vector from the origin of the target vehicle coordinate system to the origin of the updated grid map as a conversion vector; Based on the conversion vector, the laser point cloud coordinates in the grid map coordinate system are converted into grid map information in the vehicle coordinate system; Based on the odometer information of the target vehicle and the grid map information in the vehicle coordinate system, the positioning trajectory information of the target vehicle in the world coordinate system is converted.

7. An intelligent laser positioning device, characterized in that: The device comprises: A map acquisition module, which is used to acquire a grid map of the first environmental area based on first laser point cloud information and GPS positioning information in the first environmental area; A point cloud data acquisition module, which is used to acquire in real time the odometer information of the target vehicle and the second laser point cloud data in the second environmental area when the target vehicle is running in the first environmental area; wherein the second environmental area is located within the first environmental area, and the second environmental area is an area within the first environmental area where the GPS signal is poor; An updating module, configured to obtain an updated grid map based on the first laser point cloud information and the second laser point cloud data; A positioning track acquisition module, which is used to acquire the positioning track of the target vehicle based on the updated grid map and the odometer information of the target vehicle; When the target vehicle is running in the first environmental area, real-time acquisition of odometer information of the target vehicle and second laser point cloud data in the second environmental area includes the following steps: When the target vehicle is traveling in the first environmental area, the odometer information of the target vehicle is obtained in real time through the sensor installed on the target vehicle. When it is detected that the target vehicle is running into the second environmental area where the GPS signal in the first environmental area is poor, the laser radar is turned on again to obtain real-time laser point cloud data. When it is detected that the target vehicle is driving out of the second environmental area, the laser radar is turned off and the acquisition of real-time laser point cloud data is stopped. The following steps are also included: When it is detected that the target vehicle enters the second environmental area again, updating the second laser point cloud data within the second environmental area in real time; Based on the updated second laser point cloud data, obtaining an updated second environmental area map; Based on the updated second environment area map, an updated grid map is obtained.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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