Laser mapping methods and devices, laser positioning methods and devices

Abstract

This application discloses a laser mapping method and apparatus, and a laser positioning method and apparatus. The laser mapping method includes: acquiring first image data and first laser point cloud data collected by a first vehicle in a preset operating area, and fusing the two data using a preset fusion algorithm to obtain a first fusion result; determining the target and its corresponding target type in the first laser point cloud data based on the first fusion result; preprocessing the target in the first laser point cloud data according to the target type using a preset preprocessing strategy to obtain preprocessed first laser point cloud data; and constructing a point cloud map of the preset operating area based on the preprocessed first laser point cloud data and corresponding post-processed positioning data. This application fuses image data and laser point cloud data for target recognition, improving target recognition accuracy. It also employs different preprocessing operations on the laser point cloud data for different target types, thus improving the accuracy of laser mapping and positioning.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a laser mapping method and apparatus, and a laser positioning method and apparatus. Background Technology

[0002] As autonomous driving technology matures, the operating areas of Robotaxi (autonomous taxis) and Robobus (autonomous buses) are becoming more diversified. Due to various scenarios in cities that can affect the effectiveness of traditional integrated navigation and positioning, such as urban canyons and winding bridges, multi-sensor fusion (MSF) technology is being increasingly used in the positioning solutions for autonomous vehicles.

[0003] Multi-sensor fusion positioning technology mainly includes visual mapping and positioning technology and laser mapping and positioning technology. Compared with monocular or binocular visual positioning technology, laser mapping and positioning technology has a better engineering framework and higher accuracy and stability. Therefore, it is more often the preferred solution in practical applications as an auxiliary observation when GNSS (Global Navigation Satellite System) / RTK (Real-time kinematic) fails, so as to ensure centimeter-level positioning accuracy in the operating area at all times and in all weather conditions.

[0004] Ideally, point cloud matching and localization can achieve centimeter-level accuracy (within 10cm). However, if the scene during mapping differs significantly from the scene during localization—for example, if there are many vehicles near a parking lot during mapping but few during localization—the ICP (Iterative Closest Point) or NDT (Normal Distribution Transform) matching algorithms cannot calculate an effective transformation, affecting localization accuracy. The error can expand to the decimeter level, or even fail to output correct, high-confidence localization results, thus impacting the normal operation of autonomous vehicles. Summary of the Invention

[0005] This application provides a laser mapping method and apparatus, and a laser positioning method and apparatus, to improve the accuracy of laser mapping and positioning.

[0006] The embodiments of this application adopt the following technical solutions:

[0007] In a first aspect, embodiments of this application provide a laser mapping method, wherein the laser mapping method includes:

[0008] First image data and first laser point cloud data collected by the first vehicle in a preset operating area are acquired, and the first image data and the first laser point cloud data are fused using a preset fusion algorithm to obtain a first fusion result;

[0009] Based on the first fusion result, determine the target and its corresponding target type in the first laser point cloud data;

[0010] Based on the target type corresponding to the target in the first laser point cloud data, the target in the first laser point cloud data is preprocessed using a preset preprocessing strategy to obtain the preprocessed first laser point cloud data.

[0011] A point cloud map of the preset operating area is constructed based on the preprocessed first laser point cloud data and the post-processed positioning data corresponding to the first laser point cloud data.

[0012] Optionally, the target includes foreground targets and background targets, the background targets include variable background targets and immutable background targets, and the step of preprocessing the targets in the first laser point cloud data according to the target type corresponding to the targets in the first laser point cloud data using a preset preprocessing strategy to obtain preprocessed first laser point cloud data includes:

[0013] The foreground target is removed from the first laser point cloud data, and the point cloud weights of the variable background target and the non-variable background target in the first laser point cloud data are set respectively, wherein the point cloud weight of the variable background target after setting is less than the point cloud weight of the non-variable background target after setting, so as to obtain the preprocessed first laser point cloud data.

[0014] Optionally, after constructing the point cloud map of the preset operating area based on the preprocessed first laser point cloud data and the post-processed positioning data corresponding to the first laser point cloud data, the method further includes:

[0015] Obtain the laser positioning results of the second vehicle in the preset operating area and obtain the set of missing point cloud locations in the preset operating area;

[0016] Based on the laser positioning results of the second vehicle in the preset operating area and the set of missing point cloud locations in the preset operating area, it is determined whether to update the point cloud map of the preset operating area.

[0017] Optionally, obtaining the set of missing point cloud locations for the preset operating area includes:

[0018] Determine the current scene area where the first vehicle is located, wherein the scene area is located within the preset operating area;

[0019] Based on the targets and their corresponding target types in the first laser point cloud data, determine the relative number of foreground targets in the first laser point cloud data;

[0020] Based on the scene area where the first vehicle is currently located and the relative number of foreground targets, a set of missing point cloud locations for the preset operating area is constructed.

[0021] Optionally, constructing the set of missing point cloud locations for the preset operating area based on the scene area where the first vehicle is currently located and the relative number of foreground targets includes:

[0022] Data on the scene area where the first vehicle is currently located is obtained based on high-precision map data, wherein the scene area data includes scene area type and scene area location;

[0023] The point cloud map update identifier is determined based on the relative number of the foreground targets, and the point cloud map update identifier includes an updatable identifier and a non-updatable identifier;

[0024] Based on the data of the scene area where the first vehicle is currently located and the point cloud map update identifier, a set of missing point cloud locations for the preset operating area is constructed.

[0025] Optionally, determining the point cloud map update identifier based on the relative number of the foreground targets includes:

[0026] If the relative number of foreground targets is greater than a preset relative number threshold, then the point cloud map update identifier is determined to be the non-updateable identifier;

[0027] If the relative number of foreground targets is not greater than a preset relative number threshold, then the point cloud map update identifier is determined to be the updateable identifier.

[0028] Optionally, the laser positioning result of the second vehicle in the preset operating area includes the laser positioning location and the laser positioning confidence level, and the set of missing point cloud locations includes a point cloud map update identifier. The step of determining whether to update the point cloud map of the preset operating area based on the laser positioning result of the second vehicle in the preset operating area and the set of missing point cloud locations in the preset operating area includes:

[0029] If the laser positioning confidence is greater than the preset map update threshold, the laser positioning location is located within the set of missing point cloud locations and the corresponding point cloud map update identifier is an updateable identifier, then it is determined to update the point cloud map of the preset operating area, and the point cloud map of the preset operating area is updated according to the laser positioning result of the second vehicle in the preset operating area.

[0030] Otherwise, it is determined that the point cloud map of the preset operating area will not be updated.

[0031] Secondly, embodiments of this application also provide a laser positioning method, wherein the laser positioning method includes:

[0032] Acquire third image data and third laser point cloud data collected by a third vehicle in a preset operating area, and fuse the third image data and the third laser point cloud data using a preset fusion algorithm to obtain a third fusion result;

[0033] The targets and their corresponding target types in the third laser point cloud data are determined based on the third fusion result.

[0034] Based on the target type corresponding to the target in the third laser point cloud data, the target in the third laser point cloud data is preprocessed using a preset preprocessing strategy to obtain the preprocessed second laser point cloud data.

[0035] The laser positioning result of the third vehicle in the preset operating area is determined based on the preprocessed second laser point cloud data and the point cloud map of the preset operating area.

[0036] The point cloud map of the preset operating area is obtained based on any of the aforementioned laser mapping methods.

[0037] Optionally, after acquiring the third image data and third laser point cloud data collected by the third vehicle in the preset operating area, the method further includes:

[0038] Obtain the set of missing point cloud locations in the preset operating area. The set of missing point cloud locations includes point cloud map update identifiers, and the point cloud map update identifiers include non-updateable identifiers.

[0039] Based on the set of missing point cloud locations in the preset operating area and the post-processed positioning data corresponding to the third laser point cloud data, it is determined whether the third vehicle has entered the scene area corresponding to the non-updateable identifier.

[0040] If so, the laser positioning result of the third vehicle in the preset operating area is determined using the laser SLAM algorithm based on the third laser point cloud data.

[0041] Thirdly, embodiments of this application also provide a laser mapping device, wherein the laser mapping device includes:

[0042] The first fusion unit is used to acquire first image data and first laser point cloud data collected by the first vehicle in a preset operating area, and to fuse the first image data and the first laser point cloud data using a preset fusion algorithm to obtain a first fusion result.

[0043] The first determining unit is used to determine the target and the corresponding target type in the first laser point cloud data based on the first fusion result.

[0044] The first preprocessing unit is used to preprocess the target in the first laser point cloud data according to the target type corresponding to the target in the first laser point cloud data using a preset preprocessing strategy to obtain the preprocessed first laser point cloud data.

[0045] The construction unit is used to construct a point cloud map of the preset operating area based on the preprocessed first laser point cloud data and the post-processed positioning data corresponding to the first laser point cloud data.

[0046] Fourthly, embodiments of this application also provide a laser positioning device, wherein the laser positioning device includes:

[0047] The second fusion unit is used to acquire third image data and third laser point cloud data collected by the third vehicle in a preset operating area, and to fuse the third image data and the third laser point cloud data using a preset fusion algorithm to obtain a third fusion result.

[0048] The second determining unit is used to determine the target and the corresponding target type in the third laser point cloud data based on the third fusion result.

[0049] The second preprocessing unit is used to preprocess the targets in the third laser point cloud data according to the target type corresponding to the targets in the third laser point cloud data, and to obtain the preprocessed second laser point cloud data.

[0050] The first positioning unit is used to determine the laser positioning result of the third vehicle in the preset operating area based on the preprocessed second laser point cloud data and the point cloud map of the preset operating area.

[0051] The point cloud map of the preset operating area is obtained based on the aforementioned laser mapping device.

[0052] Fifthly, embodiments of this application also provide an electronic device, including:

[0053] Processor; and

[0054] A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform any of the methods described above.

[0055] Sixthly, embodiments of this application also provide a computer-readable storage medium storing one or more programs that, when executed by an electronic device including multiple applications, cause the electronic device to perform any of the methods described above.

[0056] The at least one technical solution adopted in the embodiments of this application can achieve the following beneficial effects: The laser mapping method of the embodiments of this application first acquires first image data and first laser point cloud data collected by a first vehicle in a preset operating area, and then uses a preset fusion algorithm to fuse the first image data and the first laser point cloud data to obtain a first fusion result; then, based on the first fusion result, the target and the corresponding target type in the first laser point cloud data are determined; then, based on the target type corresponding to the target in the first laser point cloud data, a preset preprocessing strategy is used to preprocess the target in the first laser point cloud data to obtain preprocessed first laser point cloud data; finally, a point cloud map of the preset operating area is constructed based on the preprocessed first laser point cloud data and the post-processed positioning data corresponding to the first laser point cloud data. The laser mapping method of the embodiments of this application fuses image data and laser point cloud data for target recognition, which improves the target recognition accuracy relying solely on laser point cloud data. Furthermore, it determines the target type based on the fusion result and performs different preprocessing operations on the laser point cloud data for different target types, thereby improving the accuracy of the point cloud map. Attached Figure Description

[0057] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0058] Figure 1 This is a schematic flowchart of a laser mapping method according to an embodiment of this application;

[0059] Figure 2 This is a schematic diagram of the structure of a laser mapping device according to an embodiment of this application;

[0060] Figure 3 This is a flowchart illustrating a laser positioning method according to an embodiment of this application;

[0061] Figure 4 This is a schematic diagram of the structure of a laser positioning device according to an embodiment of this application;

[0062] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0064] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0065] This application provides a laser mapping method, such as... Figure 1 The diagram provided illustrates a flow chart of a laser mapping method according to an embodiment of this application. The laser mapping method includes at least the following steps S110 to S140:

[0066] Step S110: Obtain first image data and first laser point cloud data collected by the first vehicle in the preset operating area, and use a preset fusion algorithm to fuse the first image data and the first laser point cloud data to obtain a first fusion result.

[0067] In constructing a point cloud map, this embodiment of the application requires first acquiring first image data and first laser point cloud data collected by a first vehicle within a preset operating area. The first vehicle can be a data acquisition vehicle equipped with sensors such as ground truth analyzers, cameras, and lidar, and all sensors have been pre-calibrated. The preset operating area can be flexibly determined according to the actual application scenario, for example, based on the operating service range of Robotaxi or Robobus.

[0068] The first image data is acquired by a camera on the data acquisition vehicle, and the first laser point cloud data is acquired by a lidar on the data acquisition vehicle. Since the data output frequencies of the camera and lidar are different, the image data acquired by the camera and the laser point cloud data acquired by the lidar can be time-synchronized to ensure the accuracy of subsequent data processing. That is, the first image data and the first laser point cloud data here can be regarded as data after time synchronization.

[0069] Then, a preset fusion algorithm is used to fuse the first image data and the first laser point cloud data to obtain the first fusion result. The preset fusion algorithm can be flexibly selected based on existing image-laser point cloud fusion algorithms, such as target detection and recognition based on deep learning algorithms. Since target recognition algorithms based on laser point cloud data have low accuracy, especially for small targets such as pedestrians and bicycles, using the fusion result of image data and laser point cloud data for target detection and recognition can improve the recognition accuracy using only laser point cloud data, and further improve the accuracy of mapping and localization.

[0070] Step S120: Determine the target and its corresponding target type in the first laser point cloud data based on the first fusion result.

[0071] Based on the fusion result of image data and corresponding laser point cloud data, the target in the first laser point cloud data and the corresponding target type can be identified. The target type can include foreground targets and background targets.

[0072] Step S130: Based on the target type corresponding to the target in the first laser point cloud data, preprocess the target in the first laser point cloud data using a preset preprocessing strategy to obtain the preprocessed first laser point cloud data.

[0073] Ideally, the positioning accuracy achieved based on point cloud maps can reach the centimeter level. However, if the scene during mapping differs significantly from the scene during positioning, it will affect the positioning results and accuracy. The magnitude of the scene change is related to the objects present in the scene and the types of objects. For example, if there are many background objects and few dynamic foreground objects, the scene change will be relatively small. Conversely, if there are few background objects and many dynamic foreground objects, the scene change will be relatively large.

[0074] Based on this, embodiments of this application can adopt certain preprocessing strategies to preprocess the targets in the first laser point cloud data according to the different target types contained in the first laser point cloud data, so as to obtain the preprocessed first laser point cloud data. The principle of preprocessing is to minimize the impact of scene differences on laser point cloud mapping and positioning accuracy.

[0075] Step S140: Construct a point cloud map of the preset operating area based on the preprocessed first laser point cloud data and the post-processed positioning data corresponding to the first laser point cloud data.

[0076] After obtaining the preprocessed first laser point cloud data, it is necessary to obtain the corresponding positioning ground value through post-processing algorithm. Based on the positioning ground value, the preprocessed first laser point cloud data is mapped. Since the positioning accuracy of the post-processed positioning ground value is generally within 1cm, this positioning ground value can be directly used to perform point cloud projection, matching and other operations to generate a point cloud map of the preset operating area.

[0077] In some embodiments of this application, the target includes a foreground target and a background target, and the background target includes a variable background target and a non-variable background target. The step of preprocessing the targets in the first laser point cloud data according to the target type corresponding to the target in the first laser point cloud data using a preset preprocessing strategy to obtain preprocessed first laser point cloud data includes: removing the foreground target from the first laser point cloud data, and setting the point cloud weights of the variable background target and the non-variable background target in the first laser point cloud data respectively, wherein the set point cloud weight of the variable background target is less than the set point cloud weight of the non-variable background target, thereby obtaining the preprocessed first laser point cloud data.

[0078] The targets in this application embodiment are mainly divided into foreground targets and background targets. Foreground targets are dynamic targets such as vehicles and pedestrians, while background targets are signs, trees, and tall buildings on both sides of the road. Therefore, background targets can be further divided into variable background targets and non-variable background targets. Variable background targets are, for example, trees on both sides of the road, which generally change with the seasons, but have a lower frequency of change compared to foreground targets. Non-variable background targets are, for example, tall buildings on both sides of the road, which generally do not change over time, or can be regarded as targets with the lowest frequency of change. Therefore, different targets have different frequencies of change in the corresponding scenarios.

[0079] Therefore, to minimize the impact of scene variations on laser mapping and positioning accuracy, point cloud map construction can rely more on laser point cloud data of immutable background targets and less on foreground and variable background targets. For example, foreground targets in the first laser point cloud data can be directly removed, and the point cloud map can be constructed solely based on the laser point cloud data of background targets, thus avoiding the impact of dynamic foreground targets on mapping and positioning accuracy. Furthermore, the point cloud weights of variable background targets in the first laser point cloud data can be reduced (e.g., set to 0.5), while the point cloud weights of immutable background targets can be increased (e.g., set to 1), thereby appropriately reducing the influence of variable background target laser point cloud data and fully utilizing the immutable background target laser point cloud data to improve laser mapping and positioning accuracy.

[0080] In some embodiments of this application, after constructing the point cloud map of the preset operating area based on the preprocessed first laser point cloud data and the post-processed positioning data corresponding to the first laser point cloud data, the method further includes: obtaining the laser positioning result of the second vehicle in the preset operating area and obtaining the set of missing point cloud locations in the preset operating area; and determining whether to update the point cloud map of the preset operating area based on the laser positioning result of the second vehicle in the preset operating area and the set of missing point cloud locations in the preset operating area.

[0081] Because the number of foreground targets in real-world scenarios varies—for example, there are relatively more foreground targets near parking lots and subway entrances, while there are relatively fewer foreground targets in driving areas—foreground targets in different scenarios will cause varying degrees of background occlusion. After removing the laser point cloud data of foreground targets, varying degrees of background point cloud loss will occur. Therefore, a set of missing point cloud locations for a preset operating area can be established based on different target recognition results. This set of missing point cloud locations, along with the actual laser positioning results of a second vehicle in the preset operating area, can then be used to update the point cloud map. The second vehicle can refer to a vehicle actually operating in the operating area. Since there are many operating vehicles, the actual positioning results of these vehicles can be used to continuously compensate for and improve the background point cloud data, thereby improving the completeness and positioning accuracy of the point cloud map.

[0082] In some embodiments of this application, obtaining the set of missing point cloud locations in the preset operating area includes: determining the scene area where the first vehicle is currently located, the scene area being located within the preset operating area; determining the relative number of foreground targets in the first laser point cloud data based on the targets and corresponding target types in the first laser point cloud data; and constructing the set of missing point cloud locations in the preset operating area based on the scene area where the first vehicle is currently located and the relative number of foreground targets.

[0083] As mentioned earlier, the relative number of foreground targets varies in different scene areas. For example, there are relatively more foreground targets near parking lots and subway entrances / exits, while there are relatively fewer foreground targets in driving sections. This results in different degrees of occlusion of the background by foreground targets in different scene areas. Therefore, in constructing the point cloud missing location set of the preset operating area in this embodiment, the scene area where the first vehicle is currently located can be determined first, such as whether it is a parking lot area, a subway entrance / exit area, or a normal driving section area. Specifically, the scene area where the first vehicle is currently located can be determined based on the current ground truth value obtained after post-processing and the high-precision map data.

[0084] Based on the targets and their corresponding target types in the first laser point cloud data, the relative number of foreground targets in the first laser point cloud data can be determined. For example, the percentage of laser point cloud data containing foreground targets in the entire laser point cloud data can be counted. If the percentage of foreground targets is higher, it means that there are more foreground targets and fewer background targets. Conversely, if the percentage is lower, it means that there are fewer foreground targets and more background targets. This can then be used to construct a set of missing point cloud locations for a preset operating area, which may include information such as scene area type and the absolute location of the missing location.

[0085] In some embodiments of this application, constructing the set of missing point cloud locations for the preset operating area based on the scene area where the first vehicle is currently located and the relative number of foreground targets includes: obtaining data of the scene area where the first vehicle is currently located based on high-precision map data, wherein the scene area data includes scene area type and scene area location; determining a point cloud map update identifier based on the relative number of foreground targets, wherein the point cloud map update identifier includes an updatable identifier and a non-updatable identifier; and constructing the set of missing point cloud locations for the preset operating area based on the scene area where the first vehicle is currently located and the point cloud map update identifier.

[0086] High-precision map data can provide absolute location information for different road segments and different scene areas. Therefore, data on the scene area where the first vehicle is currently located can be obtained from the high-precision map data, including scene area type and scene area location. Scene area type can be represented and distinguished by scene area identifier, and scene area location refers to the set of absolute locations corresponding to the scene area.

[0087] Furthermore, the relative number of foreground targets reflects their degree of occlusion of the background area in the scene. Here, the relative number of foreground targets in the entire scene area can be considered comprehensively. Based on the relative number of foreground targets in the entire scene area, it can be determined whether the point cloud map of the scene area can be updated. Finally, the set of missing point cloud locations of the preset operation area is generated by combining the scene area type and scene area location.

[0088] In some embodiments of this application, determining the point cloud map update identifier based on the relative number of foreground targets includes: if the relative number of foreground targets is greater than a preset relative number threshold, then the point cloud map update identifier is determined to be the non-updatable identifier; if the relative number of foreground targets is not greater than the preset relative number threshold, then the point cloud map update identifier is determined to be the updatable identifier.

[0089] The more foreground targets there are, the fewer background targets there are. For example, areas near parking lots or subway entrances are typical scenarios with many foreground targets. In such areas, using a pre-built point cloud map for localization is not very meaningful because it's difficult to maintain accurate matching through continuous supplementation and improvement of the background point cloud. Therefore, point cloud map updates for these areas can be defined as non-updatable. When a vehicle enters this area, the point cloud map can be left unupdated, and other localization strategies can be switched, such as laser SLAM (Simultaneous Localization and Mapping).

[0090] Conversely, if the relative number of foreground targets is smaller, then the relative number of background targets is larger. For example, in normal driving sections, although laser mapping and localization in these areas are also affected by foreground targets, the point cloud map can be continuously updated by supplementing and improving the background point cloud data, thereby ensuring the accuracy of matching and localization. Therefore, the point cloud map update markers for these areas can be defined as updateable markers. When a vehicle drives into this area, the point cloud map can be updated based on the localization results.

[0091] In some embodiments of this application, the laser positioning result of the second vehicle in the preset operating area includes a laser positioning location and a laser positioning confidence level. The set of missing point cloud locations includes a point cloud map update identifier. Determining whether to update the point cloud map of the preset operating area based on the laser positioning result of the second vehicle in the preset operating area and the set of missing point cloud locations in the preset operating area includes: if the laser positioning confidence level is greater than a preset map update threshold, the laser positioning location is within the set of missing point cloud locations, and the corresponding point cloud map update identifier is an updateable identifier, then it is determined that the point cloud map of the preset operating area should be updated, and the point cloud map of the preset operating area should be updated based on the laser positioning result of the second vehicle in the preset operating area; otherwise, it is determined that the point cloud map of the preset operating area should not be updated.

[0092] For updating the point cloud map, in addition to considering the set of missing point cloud locations in the preset operating area constructed in the above embodiments, the actual laser positioning results of the vehicle can also be considered. Since the foreground targets detected by different vehicles when they reach the same location are not completely consistent, the point cloud map can be updated in real time based on the laser positioning results. For example, if a foreground target occludes a background area during the mapping stage, but the background area is not occluded during the positioning stage, the point cloud map can be updated based on the point cloud data at that location, for example, by compensating for missing background point cloud data.

[0093] The laser positioning results mainly include the laser positioning location and the laser positioning reliability. The laser positioning location can be obtained by matching the current laser point cloud data with the point cloud map based on existing point cloud matching algorithms such as ICP or NDT. The laser positioning reliability can be evaluated based on existing confidence evaluation algorithms such as Autoware's NDT matching probability score. If the laser positioning reliability is greater than the preset effective positioning threshold, it means that the current positioning result meets the positioning accuracy requirements, and is therefore a valid positioning result.

[0094] If the laser positioning reliability is greater than the preset effective positioning threshold, it can be further determined whether it has reached the preset map update threshold. The preset effective positioning threshold and the preset map update threshold can be set to different values. Both can be calculated offline. The preset map update threshold can be higher than the preset effective positioning threshold because when updating the point cloud map, higher stability and accuracy are required. At the same time, with a large number of operating vehicles, a high map update frequency is not necessary.

[0095] If the laser positioning confidence level is greater than the preset map update threshold, and the laser positioning location is within the set of missing point cloud locations, and the corresponding point cloud map update flag is updatable, then the point cloud map of the preset operating area can be updated using the current laser positioning results. If the laser positioning confidence level is not greater than the preset map update threshold, or the laser positioning location is not within the set of missing point cloud locations, or the corresponding point cloud map update flag is non-updatable, then no point cloud map update will be performed.

[0096] This application embodiment utilizes the data returned after the operating vehicle successfully locates itself on the point cloud map to update the point cloud map in real time, ensuring the real-time performance of the point cloud map. This eliminates the need for periodic data collection and point cloud map creation by the vehicle, improving mapping efficiency and reducing mapping costs.

[0097] This application also provides a laser mapping device 200, such as... Figure 2 As shown, a schematic diagram of a laser mapping device according to an embodiment of this application is provided. The laser mapping device 200 includes: a first fusion unit 210, a first determination unit 220, a first preprocessing unit 230, and a construction unit 240, wherein:

[0098] The first fusion unit 210 is used to acquire first image data and first laser point cloud data collected by the first vehicle in a preset operating area, and to fuse the first image data and the first laser point cloud data using a preset fusion algorithm to obtain a first fusion result.

[0099] The first determining unit 220 is used to determine the target and the corresponding target type in the first laser point cloud data based on the first fusion result.

[0100] The first preprocessing unit 230 is used to preprocess the target in the first laser point cloud data according to the target type corresponding to the target in the first laser point cloud data, and to obtain the preprocessed first laser point cloud data.

[0101] The construction unit 240 is used to construct a point cloud map of the preset operating area based on the preprocessed first laser point cloud data and the post-processed positioning data corresponding to the first laser point cloud data.

[0102] In some embodiments of this application, the target includes a foreground target and a background target, and the background target includes a variable background target and an immutable background target. The first preprocessing unit 230 is specifically used to: remove the foreground target from the first laser point cloud data, and set the point cloud weights of the variable background target and the immutable background target in the first laser point cloud data respectively, wherein the set point cloud weight of the variable background target is less than the set point cloud weight of the immutable background target, thereby obtaining the preprocessed first laser point cloud data.

[0103] In some embodiments of this application, the laser positioning device further includes: a first acquisition unit, configured to acquire the laser positioning result of the second vehicle in the preset operating area and acquire the set of missing point cloud locations in the preset operating area; and a third determination unit, configured to determine whether to update the point cloud map of the preset operating area based on the laser positioning result of the second vehicle in the preset operating area and the set of missing point cloud locations in the preset operating area.

[0104] In some embodiments of this application, the first acquisition unit is specifically used to: determine the scene area where the first vehicle is currently located, the scene area being located within the preset operating area; determine the relative number of foreground targets in the first laser point cloud data based on the targets and corresponding target types in the first laser point cloud data; and construct a set of missing point cloud locations in the preset operating area based on the scene area where the first vehicle is currently located and the relative number of foreground targets.

[0105] In some embodiments of this application, the first acquisition unit is specifically used to: acquire data of the scene area where the first vehicle is currently located based on high-precision map data, wherein the scene area data includes scene area type and scene area location; determine point cloud map update identifiers based on the relative number of foreground targets, wherein the point cloud map update identifiers include updateable identifiers and non-updateable identifiers; and construct a set of missing point cloud locations for the preset operating area based on the data of the scene area where the first vehicle is currently located and the point cloud map update identifiers.

[0106] In some embodiments of this application, the first acquisition unit is specifically used to: if the relative number of the foreground targets is greater than a preset relative number threshold, then determine the point cloud map update identifier as the non-updatable identifier; if the relative number of the foreground targets is not greater than the preset relative number threshold, then determine the point cloud map update identifier as the updatable identifier.

[0107] In some embodiments of this application, the laser positioning result of the second vehicle in the preset operating area includes the laser positioning location and the laser positioning confidence level. The set of missing point cloud locations includes a point cloud map update identifier. The third determining unit is specifically used to: if the laser positioning confidence level is greater than a preset map update threshold, the laser positioning location is located within the set of missing point cloud locations, and the corresponding point cloud map update identifier is an updateable identifier, then determine to update the point cloud map of the preset operating area, and update the point cloud map of the preset operating area according to the laser positioning result of the second vehicle in the preset operating area; otherwise, determine not to update the point cloud map of the preset operating area.

[0108] It is understood that the laser mapping device described above can realize all the steps of the laser mapping method provided in the foregoing embodiments. The relevant explanations of the laser mapping method are applicable to the laser mapping device, and will not be repeated here.

[0109] This application also provides a laser positioning method, such as... Figure 3 The diagram shows a flowchart of a laser positioning method according to an embodiment of this application. The laser positioning method includes at least the following steps S310 to S340:

[0110] S310: Acquire third image data and third laser point cloud data collected by the third vehicle in the preset operating area, and use a preset fusion algorithm to fuse the third image data and the third laser point cloud data to obtain a third fusion result.

[0111] S320, determine the target and corresponding target type in the third laser point cloud data based on the third fusion result.

[0112] S330, based on the target type corresponding to the target in the third laser point cloud data, a preset preprocessing strategy is used to preprocess the target in the third laser point cloud data to obtain the preprocessed second laser point cloud data.

[0113] S340, determine the laser positioning result of the third vehicle in the preset operating area based on the preprocessed second laser point cloud data and the point cloud map of the preset operating area; wherein, the point cloud map of the preset operating area is obtained based on the aforementioned laser mapping method.

[0114] Steps S310-S330 are basically the same as steps S110-S130 in the aforementioned embodiments, except that the raw data in the positioning stage comes from the actual operating third vehicle. In the laser positioning stage, after obtaining the preprocessed second laser point cloud data, a certain point cloud matching algorithm is used to match the preprocessed second laser point cloud data with the pre-established point cloud map of the preset operating area, thereby obtaining the laser positioning result of the third vehicle in the preset operating area. The point cloud map here is constructed based on the laser mapping method of the aforementioned embodiments.

[0115] Similarly, the laser positioning results of the third vehicle in the preset operating area obtained above can also be used to update the point cloud map if the preset map update threshold is met. Specifically, the third laser point cloud data generated in real time by the third vehicle can be further filtered, and only the background point cloud data remaining after matching the point cloud map in the third laser point cloud data can be retained, that is, the background point cloud data that was missing when the map was built. After processing these background point clouds, they are returned to the cloud, such as point cloud weight setting and coordinate transformation. When the operation ends, the cloud can update the point cloud map based on these data and deploy the new point cloud map when the next operation begins.

[0116] In some embodiments of this application, after acquiring third image data and third laser point cloud data collected by a third vehicle in a preset operating area, the method further includes: acquiring a set of missing point cloud locations in the preset operating area, the set of missing point cloud locations including point cloud map update markers, the point cloud map update markers including non-updateable markers; determining whether the third vehicle has entered the scene area corresponding to the non-updateable marker based on the set of missing point cloud locations in the preset operating area and the post-processed positioning data corresponding to the third laser point cloud data; if so, determining the laser positioning result of the third vehicle in the preset operating area using a laser SLAM algorithm based on the third laser point cloud data.

[0117] The laser SLAM algorithm is a simultaneous localization and mapping scheme. Unlike the laser SLAM algorithm, the laser localization scheme and the laser mapping scheme in the embodiments of this application can be regarded as two independent stages. That is, firstly, a point cloud map of the entire preset operating area is constructed, and then the constructed point cloud map of the preset operating area is used for real-time localization. The localization result can also be used to update the point cloud map if certain conditions are met.

[0118] In some special scenarios, such as near parking lots or subway entrances, there are often many foreground objects and a relatively empty background. These locations are more suitable for positioning using the laser SLAM algorithm. Therefore, in actual positioning, this application embodiment can first perform positioning based on the prior point cloud map strategy of this application embodiment. At the same time, it can combine the current positioning ground truth calculated by post-processing and the set of missing point cloud locations of the preset operating area constructed during the mapping stage to determine whether it is necessary to switch the positioning strategy, such as switching to a positioning strategy based on the laser SLAM algorithm, so as to ensure the overall positioning accuracy.

[0119] Since the set of missing point cloud locations contains the missing point cloud locations and the corresponding point cloud map update markers, it is possible to determine whether the location corresponding to the current ground truth location is a location that can or needs to be updated in the point cloud map. If the point cloud map update marker corresponding to the location is a non-updateable marker, then there is no need to update the point cloud map, and the vehicle's positioning strategy can be switched to the laser SLAM algorithm.

[0120] This application embodiment also provides a laser positioning device 400, such as... Figure 4 As shown, a schematic diagram of a laser positioning device according to an embodiment of this application is provided. The laser positioning device 400 includes: a second fusion unit 410, a second determination unit 420, a second preprocessing unit 430, and a first positioning unit 440, wherein:

[0121] The second fusion unit 410 is used to acquire third image data and third laser point cloud data collected by the third vehicle in a preset operating area, and to fuse the third image data and the third laser point cloud data using a preset fusion algorithm to obtain a third fusion result.

[0122] The second determining unit 420 is used to determine the target and the corresponding target type in the third laser point cloud data based on the third fusion result.

[0123] The second preprocessing unit 430 is used to preprocess the target in the third laser point cloud data according to the target type corresponding to the target in the third laser point cloud data, and to obtain the preprocessed second laser point cloud data.

[0124] The first positioning unit 440 is used to determine the laser positioning result of the third vehicle in the preset operating area based on the preprocessed second laser point cloud data and the point cloud map of the preset operating area.

[0125] The point cloud map of the preset operating area is obtained based on the aforementioned laser mapping device.

[0126] In some embodiments of this application, the apparatus further includes: a second acquisition unit, configured to acquire a set of missing point cloud locations in the preset operating area, the set of missing point cloud locations including point cloud map update identifiers, the point cloud map update identifiers including non-updateable identifiers; a fourth determination unit, configured to determine whether the third vehicle has entered the scene area corresponding to the non-updateable identifier based on the set of missing point cloud locations in the preset operating area and the post-processed positioning data corresponding to the third laser point cloud data; and a second positioning unit, configured to determine the laser positioning result of the third vehicle in the preset operating area based on the third laser point cloud data using a laser SLAM algorithm if the condition is met.

[0127] It is understood that the laser positioning device described above can realize all the steps of the laser positioning method provided in the foregoing embodiments. The relevant explanations of the laser positioning method are applicable to the laser positioning device and will not be repeated here.

[0128] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Please refer to it. Figure 5 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0129] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0130] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0131] The processor reads the corresponding computer program from non-volatile memory into main memory and then runs it, logically forming a laser mapping device and a laser positioning device. The processor executes the program stored in memory and specifically performs the functions described in this application. Figure 1 The method and implementation of the laser mapping apparatus disclosed in the illustrated embodiment Figure 3 The illustrated embodiment discloses a method performed by a laser positioning device.

[0132] The above is as stated in this application. Figure 1 The method and implementation of the laser mapping apparatus disclosed in the illustrated embodiment Figure 3 The laser positioning device method disclosed in the illustrated embodiments can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0133] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform... Figure 1 The method and implementation of the laser mapping device in the illustrated embodiment Figure 3 The illustrated embodiment discloses a method performed by a laser positioning device.

[0134] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0135] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0136] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0137] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0138] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0139] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0140] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0141] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0142] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0143] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A laser mapping method, wherein, The laser mapping method includes: First image data and first laser point cloud data collected by the first vehicle in a preset operating area are acquired, and the first image data and the first laser point cloud data are fused using a preset fusion algorithm to obtain a first fusion result; Based on the first fusion result, determine the target and its corresponding target type in the first laser point cloud data; Based on the target type corresponding to the target in the first laser point cloud data, the target in the first laser point cloud data is preprocessed using a preset preprocessing strategy to obtain the preprocessed first laser point cloud data. A point cloud map of the preset operating area is constructed based on the preprocessed first laser point cloud data and the post-processed positioning data corresponding to the first laser point cloud data. The target types include foreground targets and background targets; After constructing a point cloud map of the preset operating area based on the preprocessed first laser point cloud data and the post-processed positioning data corresponding to the first laser point cloud data, the method further includes: Obtain the laser positioning results of the second vehicle in the preset operating area and obtain the set of missing point cloud locations in the preset operating area; Based on the laser positioning results of the second vehicle in the preset operating area and the set of missing point cloud locations in the preset operating area, determine whether to update the point cloud map of the preset operating area; The background targets include variable background targets and non-variable background targets. The preprocessing of the targets in the first laser point cloud data according to the target type corresponding to the targets in the first laser point cloud data, using a preset preprocessing strategy, to obtain the preprocessed first laser point cloud data includes: The foreground target is removed from the first laser point cloud data, and the point cloud weights of the variable background target and the non-variable background target in the first laser point cloud data are set respectively, wherein the point cloud weight of the variable background target after setting is less than the point cloud weight of the non-variable background target after setting, so as to obtain the preprocessed first laser point cloud data.

2. The laser mapping method as described in claim 1, wherein, The process of obtaining the set of missing point cloud locations in the preset operating area includes: Determine the current scene area where the first vehicle is located, wherein the scene area is located within the preset operating area; Based on the targets and their corresponding target types in the first laser point cloud data, determine the relative number of foreground targets in the first laser point cloud data; Based on the scene area where the first vehicle is currently located and the relative number of foreground targets, a set of missing point cloud locations for the preset operating area is constructed.

3. The laser mapping method as described in claim 2, wherein, The step of constructing the set of missing point cloud locations for the preset operating area based on the scene area where the first vehicle is currently located and the relative number of foreground targets includes: Data on the scene area where the first vehicle is currently located is obtained based on high-precision map data, wherein the scene area data includes scene area type and scene area location; The point cloud map update identifier is determined based on the relative number of the foreground targets, and the point cloud map update identifier includes an updatable identifier and a non-updatable identifier; Based on the data of the scene area where the first vehicle is currently located and the point cloud map update identifier, a set of missing point cloud locations for the preset operating area is constructed.

4. The laser mapping method as described in claim 3, wherein, The step of determining the point cloud map update identifier based on the relative quantity of the foreground targets includes: If the relative number of foreground targets is greater than a preset relative number threshold, then the point cloud map update identifier is determined to be the non-updateable identifier; If the relative number of foreground targets is not greater than a preset relative number threshold, then the point cloud map update identifier is determined to be the updateable identifier.

5. The laser mapping method as described in claim 1, wherein, The laser positioning result of the second vehicle in the preset operating area includes the laser positioning location and the laser positioning reliability. The set of missing point cloud locations includes a point cloud map update identifier. The step of determining whether to update the point cloud map of the preset operating area based on the laser positioning result of the second vehicle in the preset operating area and the set of missing point cloud locations in the preset operating area includes: If the laser positioning confidence is greater than the preset map update threshold, the laser positioning location is located within the set of missing point cloud locations and the corresponding point cloud map update identifier is an updateable identifier, then it is determined to update the point cloud map of the preset operating area, and the point cloud map of the preset operating area is updated according to the laser positioning result of the second vehicle in the preset operating area. Otherwise, it is determined that the point cloud map of the preset operating area will not be updated.

6. A laser positioning method, wherein, The laser positioning method includes: Acquire third image data and third laser point cloud data collected by a third vehicle in a preset operating area, and fuse the third image data and the third laser point cloud data using a preset fusion algorithm to obtain a third fusion result; The targets and their corresponding target types in the third laser point cloud data are determined based on the third fusion result. Based on the target type corresponding to the target in the third laser point cloud data, the target in the third laser point cloud data is preprocessed using a preset preprocessing strategy to obtain the preprocessed second laser point cloud data. The laser positioning result of the third vehicle in the preset operating area is determined based on the preprocessed second laser point cloud data and the point cloud map of the preset operating area. The point cloud map of the preset operating area is obtained based on the laser mapping method described in any one of claims 1 to 5.

7. The laser positioning method as described in claim 6, wherein, After acquiring the third image data and third laser point cloud data collected by the third vehicle in the preset operating area, the method further includes: Obtain the set of missing point cloud locations in the preset operating area. The set of missing point cloud locations includes point cloud map update identifiers, and the point cloud map update identifiers include non-updateable identifiers. Based on the set of missing point cloud locations in the preset operating area and the post-processed positioning data corresponding to the third laser point cloud data, it is determined whether the third vehicle has entered the scene area corresponding to the non-updateable identifier. If so, the laser positioning result of the third vehicle in the preset operating area is determined using the laser SLAM algorithm based on the third laser point cloud data.

8. A laser mapping device, wherein, The laser mapping device includes: The first fusion unit is used to acquire first image data and first laser point cloud data collected by the first vehicle in a preset operating area, and to fuse the first image data and the first laser point cloud data using a preset fusion algorithm to obtain a first fusion result. The first determining unit is used to determine the target and the corresponding target type in the first laser point cloud data based on the first fusion result. The first preprocessing unit is used to preprocess the target in the first laser point cloud data according to the target type corresponding to the target in the first laser point cloud data using a preset preprocessing strategy to obtain the preprocessed first laser point cloud data. The construction unit is used to construct a point cloud map of the preset operating area based on the preprocessed first laser point cloud data and the post-processed positioning data corresponding to the first laser point cloud data. The target types include foreground targets and background targets; The device further includes: The first acquisition unit is used to acquire the laser positioning result of the second vehicle in the preset operating area and to acquire the set of missing point cloud locations in the preset operating area. The third determining unit is used to determine whether to update the point cloud map of the preset operating area based on the laser positioning result of the second vehicle in the preset operating area and the set of missing point cloud locations in the preset operating area. The background targets include variable background targets and immutable background targets, and the first preprocessing unit is specifically used for: The foreground target is removed from the first laser point cloud data, and the point cloud weights of the variable background target and the non-variable background target in the first laser point cloud data are set respectively, wherein the point cloud weight of the variable background target after setting is less than the point cloud weight of the non-variable background target after setting, so as to obtain the preprocessed first laser point cloud data.

9. A laser positioning device, wherein, The laser positioning device includes: The second fusion unit is used to acquire third image data and third laser point cloud data collected by the third vehicle in a preset operating area, and to fuse the third image data and the third laser point cloud data using a preset fusion algorithm to obtain a third fusion result. The second determining unit is used to determine the target and the corresponding target type in the third laser point cloud data based on the third fusion result. The second preprocessing unit is used to preprocess the targets in the third laser point cloud data according to the target type corresponding to the targets in the third laser point cloud data, and to obtain the preprocessed second laser point cloud data. The first positioning unit is used to determine the laser positioning result of the third vehicle in the preset operating area based on the preprocessed second laser point cloud data and the point cloud map of the preset operating area. The point cloud map of the preset operating area is obtained based on the laser mapping device described in claim 8.

10. An electronic device, comprising: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the laser mapping method of any one of claims 1 to 5, or the laser positioning method of any one of claims 6 to 7.

11. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the laser mapping method of any one of claims 1 to 5, or the laser positioning method of any one of claims 6 to 7.

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