Method, device and equipment for detecting quality of positioning map and readable storage medium

Abstract

The application discloses a positioning map quality detection method and device, equipment and a readable storage medium, and belongs to the computer technical field. The method comprises the following steps: acquiring initial attitude information of an automatic driving vehicle at a target time and target point cloud data, the target point cloud data being point cloud data obtained by emitting laser pulses to a target object according to the initial attitude information of the automatic driving vehicle at the target time, and the target point cloud data comprising feature information of a plurality of feature points; iteratively updating the initial attitude information according to the positioning map and the feature information of the plurality of feature points, obtaining an iteration number of the initial attitude information, and the iteration number being a number of times when the iteratively updated attitude information of the initial attitude information meets updating requirements; and determining a quality detection result of the positioning map according to the iteration number, and the quality detection result being used for indicating whether environment information in the positioning map needs to be repaired. The method advances the timing of quality detection of the positioning map.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, device, and readable storage medium for quality inspection of a location map. Background Technology

[0002] With the continuous development of computer technology, autonomous driving technology is also developing rapidly. Autonomous driving technology relies heavily on accurate positioning maps, which are a crucial indicator of the driving safety of autonomous vehicles.

[0003] However, with the continuous progress of society, the environmental information included in the positioning map is also constantly changing. Therefore, there is an urgent need for a quality inspection method for positioning maps to detect the quality of the positioning maps, and then to repair the environmental information in the positioning maps based on the quality inspection results, so that the environmental information included in the positioning maps is more consistent with the actual environmental information, and the safety of autonomous vehicles driving according to the positioning maps is higher. Summary of the Invention

[0004] This application provides a method, apparatus, device, and readable storage medium for quality inspection of location maps, which can be used to solve problems in related technologies. The technical solution is as follows:

[0005] On one hand, embodiments of this application provide a method for quality inspection of a location map, the method comprising:

[0006] The initial attitude information and target point cloud data of the autonomous vehicle at the target time are obtained. The target point cloud data is the point cloud data obtained by the autonomous vehicle emitting laser pulses to the target object according to the initial attitude information at the target time. The target point cloud data includes feature information of multiple feature points.

[0007] Based on the positioning map and the feature information of the multiple feature points, the initial attitude information is iteratively updated to obtain the number of iterations of the initial attitude information. The number of iterations refers to the number of times when the attitude information obtained by iteratively updating the initial attitude information meets the update requirements.

[0008] Based on the number of iterations, the quality inspection result of the positioning map is determined, and the quality inspection result is used to indicate whether the environmental information in the positioning map needs to be repaired.

[0009] In one possible implementation, the step of iteratively updating the initial pose information based on the positioning map and the feature information of the plurality of feature points to obtain the number of iterations of the initial pose information includes:

[0010] Based on the positioning map and the feature information of the multiple feature points, reference attitude information is determined. By emitting laser pulses toward the target object in the positioning map according to the reference attitude information, the target point cloud data can be obtained.

[0011] Based on the feature information of the multiple feature points, the initial pose information is iteratively updated to obtain the first pose information, and the relationship between the first pose information and the reference pose satisfies the relationship requirements.

[0012] The number of iterations when the first attitude information is obtained is taken as the number of iterations for the initial attitude information.

[0013] In one possible implementation, determining the reference pose information based on the positioning map and the feature information of the plurality of feature points includes:

[0014] Obtain multiple candidate pose information;

[0015] The point cloud data when laser pulses are emitted toward the target object according to each candidate posture information in the positioning map is determined, and the candidate point cloud data corresponding to each candidate posture information is obtained. Each candidate point cloud data includes feature information of multiple feature points.

[0016] The reference pose information is determined based on the feature information of multiple feature points included in the target point cloud data and the feature information of multiple feature points included in each candidate point cloud data.

[0017] In one possible implementation, the method further includes:

[0018] The loss function is invoked to determine the loss value between the first attitude information and the reference attitude information;

[0019] Based on the fact that the loss value between the first attitude information and the reference attitude information is less than the loss threshold, it is determined that the relationship between the first attitude information and the reference attitude information satisfies the relationship requirement;

[0020] Based on the fact that the loss value between the first attitude information and the reference attitude information is not less than the loss threshold, it is determined that the relationship between the first attitude information and the reference attitude information does not meet the relationship requirement.

[0021] In one possible implementation, determining the quality detection result of the positioning map based on the number of iterations includes:

[0022] Based on the fact that the number of iterations is not less than the number threshold, the quality detection result of the positioning map is determined as the first result, and the first result is used to indicate that the environmental information in the positioning map needs to be repaired.

[0023] Based on the fact that the number of iterations is less than the threshold number, the quality detection result of the positioning map is determined as the second result, which is used to indicate that the environmental information in the positioning map does not need to be repaired.

[0024] In one possible implementation, after determining the quality detection result of the positioning map as the first result based on the number of iterations not being less than a threshold, the method further includes:

[0025] Determine the target locations in the positioning map where environmental information needs to be repaired;

[0026] Display a notification message, which instructs that the environmental information of the target location in the positioning map be repaired.

[0027] In one possible implementation, determining the target location in the positioning map where the environmental information needs to be repaired includes:

[0028] The location included in the initial attitude information in the positioning map is used as the target location where the environmental information in the positioning map needs to be repaired.

[0029] On the other hand, embodiments of this application provide a quality inspection device for a location map, the device comprising:

[0030] The acquisition module is used to acquire the initial attitude information and target point cloud data of the autonomous vehicle at the target time. The target point cloud data is the point cloud data obtained by the autonomous vehicle emitting laser pulses to the target object according to the initial attitude information at the target time. The target point cloud data includes feature information of multiple feature points.

[0031] The determination module is used to iteratively update the initial attitude information based on the positioning map and the feature information of the multiple feature points to obtain the number of iterations of the initial attitude information. The number of iterations refers to the number of times when the attitude information obtained by iteratively updating the initial attitude information meets the update requirements.

[0032] The determining module is further configured to determine the quality detection result of the positioning map based on the number of iterations, and the quality detection result is used to indicate whether the environmental information in the positioning map needs to be repaired.

[0033] In one possible implementation, the determining module is configured to determine reference attitude information based on the positioning map and the feature information of the plurality of feature points; to obtain target point cloud data by emitting laser pulses toward the target object in the positioning map according to the reference attitude information; to iteratively update the initial attitude information based on the feature information of the plurality of feature points to obtain first attitude information, wherein the relationship between the first attitude information and the reference attitude satisfies a relationship requirement; and to use the iteration number when the first attitude information is obtained as the iteration number of the initial attitude information.

[0034] In one possible implementation, the determining module is configured to acquire multiple candidate pose information; determine point cloud data in the positioning map when laser pulses are emitted toward the target object according to each candidate pose information, thereby obtaining candidate point cloud data corresponding to each candidate pose information, wherein each candidate point cloud data includes feature information of multiple feature points; and determine the reference pose information based on the feature information of the multiple feature points included in the target point cloud data and the feature information of the multiple feature points included in each candidate point cloud data.

[0035] In one possible implementation, the determining module is further configured to call a loss function to determine the loss value between the first attitude information and the reference attitude information; based on the loss value between the first attitude information and the reference attitude information being less than a loss threshold, determine that the relationship between the first attitude information and the reference attitude information satisfies the relationship requirement; based on the loss value between the first attitude information and the reference attitude information not being less than the loss threshold, determine that the relationship between the first attitude information and the reference attitude information does not satisfy the relationship requirement.

[0036] In one possible implementation, the determining module is configured to determine the quality detection result of the positioning map as a first result based on the number of iterations not being less than a threshold number, wherein the first result is used to indicate that the environmental information in the positioning map needs to be repaired; and to determine the quality detection result of the positioning map as a second result based on the number of iterations being less than the threshold number, wherein the second result is used to indicate that the environmental information in the positioning map does not need to be repaired.

[0037] In one possible implementation, the determining module is further configured to determine the target location in the positioning map where the environmental information needs to be repaired;

[0038] The device further includes:

[0039] The display module is used to display notification messages, which are used to instruct the environmental information of the target location in the positioning map to be repaired.

[0040] In one possible implementation, the determining module is used to take the location included in the initial attitude information in the positioning map as the target location where the environmental information in the positioning map needs to be repaired.

[0041] On the other hand, embodiments of this application provide an electronic device, which includes a processor and a memory. The memory stores at least one piece of program code, which is loaded and executed by the processor to enable the electronic device to implement any of the above-described methods for detecting the quality of positioning maps.

[0042] On the other hand, a computer-readable storage medium is also provided, wherein at least one piece of program code is stored in the computer-readable storage medium, the at least one piece of program code being loaded and executed by a processor to enable a computer to implement any of the above-described methods for quality detection of positioning maps.

[0043] On the other hand, a computer program or computer program product is also provided, wherein the computer program or computer program product stores at least one computer instruction, which is loaded and executed by a processor to enable the computer to implement any of the above-mentioned methods for quality detection of positioning maps.

[0044] The technical solution provided in this application has at least the following beneficial effects:

[0045] The technical solution provided in this application determines the number of iterations of the initial attitude information of the autonomous vehicle at the target time in real time during the driving process of the autonomous vehicle, and then determines the quality detection result of the positioning map based on the number of iterations. This method advances the timing of the quality detection of the positioning map, which can reduce the occurrence of failures when the autonomous vehicle drives according to the positioning map, and further improve the safety of the autonomous vehicle driving according to the positioning map. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the implementation environment of a location map quality detection method provided in an embodiment of this application;

[0048] Figure 2 This is a flowchart of a method for quality detection of a positioning map provided in an embodiment of this application;

[0049] Figure 3 This is a schematic diagram of a first area provided in an embodiment of this application;

[0050] Figure 4 This is another schematic diagram of the display of the first region provided in the embodiments of this application;

[0051] Figure 5 This is a schematic diagram showing the overlapping area between a first region and a second region provided in an embodiment of this application;

[0052] Figure 6 This is a schematic diagram of the structure of a location map quality detection device provided in an embodiment of this application;

[0053] Figure 7 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application;

[0054] Figure 8 This is a schematic diagram of the structure of a server provided in an embodiment of this application. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0056] Figure 1 This is a schematic diagram illustrating the implementation environment of a location map quality detection method provided in this application embodiment, such as... Figure 1 As shown, the implementation environment includes: terminal device 101 and server 102.

[0057] In one possible implementation, the location map quality detection method provided in this application embodiment can be implemented by the terminal device 101 as the execution subject, or by the server 102 as the execution subject, or by the interaction between the terminal device 101 and the server 102. This application embodiment does not limit the implementation of this method.

[0058] The terminal device 101 can be an in-vehicle terminal of an autonomous vehicle, or a device capable of remotely controlling an in-vehicle terminal of an autonomous vehicle; this embodiment does not limit its use. The server 102 can be a single server, a server cluster consisting of multiple servers, or any of a cloud computing platform and a virtualization center; this embodiment does not limit its use. The server 102 communicates with the terminal device 101 via a wired or wireless network. The server 102 has data receiving, data processing, and data sending functions. Of course, the server 102 may also have other functions; this embodiment does not limit its use.

[0059] Terminal device 101 can refer to one of a plurality of terminal devices. This embodiment uses terminal device 101 as an example. Those skilled in the art will know that the number of terminal devices 101 can be more or less. For example, there may be only one terminal device 101, or there may be dozens or hundreds of terminal devices 101, or more. This application embodiment does not limit the number or type of terminal devices.

[0060] Those skilled in the art should understand that the terminal device 101 and server 102 described above are merely illustrative examples. Other existing or future terminal devices or servers that are applicable to this application should also be included within the scope of protection of this application, and are hereby incorporated by reference.

[0061] Based on the above implementation environment, this application provides a method for quality detection of positioning maps. Figure 2 The flowchart shown in this embodiment of the present application illustrates a method for quality inspection of a positioning map. This method can be executed by an electronic device, which may be... Figure 1 Terminal device 101 in the middle can also be Figure 1 Server 102 in the middle. For example... Figure 2 As shown, the method includes the following steps:

[0062] In step 201, the initial attitude information of the autonomous vehicle at the target time and the target point cloud data are obtained. The target point cloud data includes feature information of multiple feature points.

[0063] In the exemplary embodiments of this application, the target point cloud data is the point cloud data obtained by the autonomous vehicle emitting laser pulses at the target object according to the initial attitude information at the target time. The target object can be any object that the autonomous vehicle passes by during its driving process, and this embodiment of the application does not limit this. For example, the target object is the ground surface, wall, building, etc. The target time can be any time during the autonomous vehicle's driving process, and this embodiment of the application does not limit this either.

[0064] Initial attitude information includes, but is not limited to, the position information and angle information of the autonomous vehicle at the target time. This application does not limit the method of obtaining the initial attitude information of the autonomous vehicle at the target time. For example, an electronic device may be equipped with and run a Global Positioning System (GPS). The electronic device uses GPS to locate the autonomous vehicle, thereby obtaining the initial position information of the autonomous vehicle at the target time. Alternatively, the electronic device may use base station positioning to locate the autonomous vehicle, obtaining the initial position information of the autonomous vehicle at the target time. The initial position information of the autonomous vehicle at the target time is matched to obtain the angle information of the autonomous vehicle at the target time. The initial position information and the angle information of the autonomous vehicle at the target time are used as the initial attitude information of the autonomous vehicle at the target time. The angle information includes pitch, yaw, and roll. Pitch refers to the angle of rotation around the X-axis, yaw refers to the angle of rotation around the Y-axis, and roll refers to the angle of rotation around the Z-axis.

[0065] Optionally, the process of matching the initial position information of the autonomous vehicle at the target time to obtain the angle information of the autonomous vehicle at the target time includes: acquiring first point cloud data obtained when a laser pulse is emitted towards the target object according to the target angle information based on the initial position information, and determining the matching degree between the first point cloud data and the target point cloud data. In response to the matching degree between the first point cloud data and the target point cloud data being greater than a matching threshold, the target angle information is used as the angle information of the autonomous vehicle at the target time. The matching threshold is set based on experience or adjusted according to the implementation environment; this embodiment does not limit this.

[0066] This application does not limit the process of acquiring target point cloud data. For example, target point cloud data is acquired using a 3D (Three Dimension) scanning device. The 3D scanning device communicates with an electronic device via a wired or wireless network, and sends the target point cloud data to the electronic device so that the electronic device can acquire the target point cloud data. The 3D scanning device can be a LiDAR, a stereo camera, a time-of-flight camera, or other devices; this application does not limit this. The process of the 3D scanning device acquiring target point cloud data includes: the 3D scanning device automatically measures the feature information of multiple feature points on the surface of the target object, and then outputs target point cloud data based on the feature information of the multiple feature points. Optionally, the feature information includes at least one of coordinates, color information (Red-Green-Blue, RGB), and reflection intensity information. Of course, the feature information may also include various orientation angle information; this application does not limit this either.

[0067] The coordinates can be three-dimensional, two-dimensional, or other dimensional coordinates, and this application does not limit this. Taking three-dimensional coordinates as an example, the coordinates include coordinates in a first direction, coordinates in a second direction, and coordinates in a third direction. The angle information for each direction includes angle information in the first direction, angle information in the second direction, and angle information in the third direction. The first direction, the second direction, and the third direction are three different directions. For example, the first direction is the X direction, the second direction is the Y direction, and the third direction is the Z direction.

[0068] Taking LiDAR as an example of a 3D scanning device, LiDAR is installed on autonomous vehicles. LiDAR is a measuring device that integrates laser scanning and positioning / attitude determination systems. A LiDAR system includes a laser and a receiver. The laser generates multiple laser pulses, which are emitted onto a target object. Upon receiving the laser pulses, the target object undergoes diffuse reflection, and the receiver receives the diffusely reflected laser light. The laser records the first time the laser pulse is emitted towards the target object, and the receiver records the second time the diffusely reflected laser light is received. Based on the first and second times, the LiDAR determines the propagation time of the emitted laser pulse towards the target object; then, based on the speed of light and the propagation time, it determines the feature information of feature points on the surface of the target object caused by the emitted laser pulse. Since the laser emits multiple laser pulses towards the target object at a time, it obtains feature information from multiple feature points, and then, based on the feature information of these multiple feature points, it obtains target point cloud data.

[0069] In step 202, the initial pose information is iteratively updated based on the positioning map and the feature information of multiple feature points to obtain the number of iterations of the initial pose information.

[0070] Optionally, the number of iterations refers to the number of times the attitude information obtained by iteratively updating the initial attitude information satisfies the update requirements. The location map is acquired earlier than the target time. This application embodiment does not limit the method of acquiring the location map. For example, a driving route is planned based on the starting and ending positions. A data acquisition vehicle is controlled to drive along the route. The data acquisition vehicle is equipped with a 3D scanning device, which can acquire initial point cloud data and generate a location map based on the initial point cloud data. The location map includes the starting position, the ending position, and the driving route between the starting and ending positions. The location map reflects the surrounding environmental information of the starting position, the surrounding environmental information of the ending position, and the surrounding environmental information of the driving route between the starting and ending positions.

[0071] In one possible implementation, the process of iteratively updating the initial attitude information based on the positioning map and the feature information of multiple feature points to obtain the number of iterations of the initial attitude information includes: determining reference attitude information based on the positioning map and the feature information of multiple feature points; emitting laser pulses towards the target object in the positioning map according to the reference attitude information to obtain target point cloud data; and iteratively updating the initial attitude information based on the reference attitude information and the feature information of multiple feature points to obtain the number of iterations of the initial attitude information.

[0072] This application does not limit the method of determining reference attitude information based on a positioning map and feature information of multiple feature points. Optionally, multiple candidate attitude information is obtained; point cloud data is determined in the positioning map when laser pulses are emitted towards the target object according to each candidate attitude information, and candidate point cloud data corresponding to each candidate attitude information is obtained, where each candidate point cloud data includes feature information of multiple feature points; reference attitude information is determined based on the feature information of multiple feature points included in the target point cloud data and the feature information of multiple feature points included in each candidate point cloud data.

[0073] For example, the process of determining reference pose information based on the feature information of multiple feature points included in the target point cloud data and the feature information of multiple feature points included in each candidate point cloud data includes: determining the similarity between the feature information of multiple feature points included in any candidate point cloud data and the feature information of multiple feature points included in the target point cloud data, and using the candidate pose information corresponding to the candidate point cloud data whose similarity meets the similarity requirement as the reference pose information. In one possible implementation, the similarity requirement can be the highest similarity or the similarity greater than a similarity threshold; this application embodiment does not limit this. The similarity threshold can be set based on experience or adjusted according to the implementation environment; this application embodiment does not limit this either.

[0074] In one possible implementation, after determining the reference pose information, the process of iteratively updating the initial pose information based on the reference pose information and the feature information of multiple feature points to obtain the number of iterations of the initial pose information includes: iteratively updating the initial pose information according to the feature information of multiple feature points to obtain the first pose information, wherein the relationship between the first pose information and the reference pose information satisfies the relationship requirement; and taking the number of iterations when the first pose information is obtained as the number of iterations of the initial pose information.

[0075] The process of iteratively updating the initial pose information based on the feature information of multiple feature points to obtain the first pose information includes: inputting the initial pose information and the feature information of multiple feature points into an optimization model, and iteratively updating the initial pose information through the optimization model to obtain the first pose information. Optionally, the optimization model can be a logistic regression model.

[0076] In one possible implementation, the process of determining whether the relationship between the first attitude information and the reference attitude information satisfies the relationship requirement includes: calling a loss function to determine the loss value between the first attitude information and the reference attitude information; determining that the relationship between the first attitude information and the reference attitude information satisfies the relationship requirement based on the loss value between them being less than a loss threshold; and determining that the relationship between the first attitude information and the reference attitude information does not satisfy the relationship requirement based on the loss value between them being not less than the loss threshold.

[0077] The loss function can be a perceptron loss function, a quadratic loss function, a hinge loss function, or other types of loss functions; this application embodiment does not limit this. Optionally, the loss threshold can be set based on experience or adjusted according to the implementation environment; this application embodiment does not limit this. For example, the loss threshold is 0.1.

[0078] For example, based on the feature information of multiple feature points, the initial pose information is updated once to obtain the second pose information, and the loss value between the second pose information and the reference pose information is determined. If the loss value between the second pose information and the reference pose information is less than a loss threshold, the second pose information is used as the first pose information, and the iteration count for the initial pose information is determined to be 1. If the loss value between the second pose information and the reference pose information is not less than the loss threshold, the initial pose information is updated a second time based on the feature information of multiple feature points to obtain the third pose information, and the loss value between the third pose information and the reference pose information is determined. If the loss value between the third pose information and the reference pose information is less than the loss threshold, the third pose information is used as the first pose information, and the iteration count for the initial pose information is determined to be 2. If the loss value between the third pose information and the reference pose information is not less than the loss threshold, the initial pose information continues to be iteratively updated until the loss value between the iteratively updated pose information and the reference pose information is less than the loss threshold. The iteratively updated pose information is then used as the first pose information, and the iteration count for obtaining the iteratively updated pose information is taken as the iteration count for the initial pose information.

[0079] Optionally, the differences between the first attitude information and the reference attitude information in each dimension can also be determined. If the differences between the first attitude information and the reference attitude information in each dimension are all less than a difference threshold, then the relationship between the first attitude information and the reference attitude information is determined to meet the relationship requirements. If there is a dimension in which the differences between the first attitude information and the reference attitude information in each dimension are less than the difference threshold, then the relationship between the first attitude information and the reference attitude information is determined to not meet the relationship requirements. The difference threshold is set based on experience or adjusted according to the implementation environment; this embodiment does not limit this.

[0080] It should be noted that the 3D scanning equipment of the autonomous vehicle emits a laser pulse at regular intervals to obtain the point cloud data corresponding to each laser pulse. Then, it acquires the autonomous vehicle's attitude information at the time of the laser pulse emission, thereby determining the number of iterations of the autonomous vehicle's attitude information at each laser pulse emission. Based on this number of iterations, the quality of the localization map is determined. Factors influencing the determination of the number of iterations for each laser pulse emission include: the autonomous vehicle's attitude information at each laser pulse emission, the laser's measurement accuracy, and the quality of the localization map. The autonomous vehicle's attitude information at each laser pulse emission is determined via GPS or base station, thus ensuring a relatively consistent quality across different laser pulse emissions. The laser's measurement accuracy is limited by its hardware and environmental conditions. Since the time interval between two adjacent laser pulse emissions is very short, the laser's hardware and environmental conditions remain largely unchanged and can be disregarded. The quality of the localization map is related to its ability to describe the environment. A weaker ability to describe the environment results in a lower-quality localization map, while a stronger ability results in a higher-quality localization map. Therefore, the main factor affecting the number of iterations of attitude information for autonomous vehicles is the quality of the localization map.

[0081] In step 203, the quality inspection result of the positioning map is determined based on the number of iterations. The quality inspection result is used to indicate whether the environmental information in the positioning map needs to be repaired.

[0082] In one possible implementation, a high number of iterations indicates a significant discrepancy between the actual environmental information of the location included in the initial pose information and the environmental information of the location included in the initial pose information in the positioning map. In other words, there is a large difference between the environmental information of the location included in the initial pose information when acquiring the positioning map and the environmental information of the location included in the initial pose information after acquiring the positioning map. Therefore, the environmental information in the positioning map needs to be repaired. Conversely, a low number of iterations indicates a small discrepancy between the actual environmental information of the location included in the initial pose information and the environmental information of the location included in the initial pose information in the positioning map. In other words, there is little difference between the environmental information of the location included in the initial pose information when acquiring the positioning map and the environmental information of the location included in the initial pose information after acquiring the positioning map. Therefore, there is no need to repair the environmental information in the positioning map. Thus, detecting the quality of the positioning map based on the number of iterations is more objective and accurate. Furthermore, repairing the positioning map based on the quality detection results can improve its accuracy to some extent, making the environmental information in the positioning map more consistent with the actual environmental information, thereby improving the driving safety and positioning accuracy of autonomous vehicles.

[0083] Optionally, based on the number of iterations not being less than a threshold, the quality detection result of the positioning map is determined as the first result, which indicates that the environmental information in the positioning map needs to be repaired. Based on the number of iterations being less than the threshold, the quality detection result of the positioning map is determined as the second result, which indicates that the environmental information in the positioning map does not need to be repaired. The threshold can be set based on experience or adjusted according to the implementation environment; this embodiment does not limit this. For example, the threshold is 10.

[0084] Because the location map of the target area is collected earlier than the target time, there may be a situation where the environment of the target area is adjusted after the location map is collected. In this case, since the location map of the target area has already been collected, the location map of the target area after the environment is adjusted will not be collected again. This will result in a difference between the environmental information in the location map of the target area and the actual environmental information of the target area.

[0085] For example, a location map of region A was collected on March 1, 2022. There were no objects at location B in the location map of region A. On April 1, 2022, a wall was built at location B in region A. This caused a difference between the environmental information of location B in the location map and the actual environmental information of location B in region A.

[0086] In one possible implementation, if the electronic device is a terminal device, the terminal device determines the quality detection result of the positioning map. After the quality detection result of the positioning map is the first result, it can also determine the target location in the positioning map where the environmental information needs to be repaired, and then display a notification message. The notification message is used to instruct the environmental information of the target location in the positioning map to be repaired.

[0087] If the electronic device is a server, the server determines the quality inspection result of the positioning map. After the quality inspection result of the positioning map is the first result, it can also determine the target location in the positioning map where the environmental information needs to be repaired. Then, it sends a notification message to the terminal device and displays the notification message, which is used to instruct the environmental information of the target location in the positioning map to be repaired.

[0088] The process of determining the target location in the positioning map where environmental information needs to be repaired includes: taking the location included in the initial attitude information in the positioning map as the target location in the positioning map where environmental information needs to be repaired.

[0089] Optionally, the difference between the initial attitude information and the first attitude information can also be determined; the quality detection result of the localization map is determined based on the number of iterations and the difference between the initial attitude information and the first attitude information. Specifically, the number of iterations for the initial attitude information can be determined first, followed by the difference between the initial attitude information and the first attitude information; alternatively, the difference between the initial attitude information and the first attitude information can be determined first, followed by the number of iterations for the initial attitude information. This embodiment does not limit the order of determining the number of iterations for the initial attitude information or the order of determining the difference between the initial attitude information and the first attitude information.

[0090] In one possible implementation, the process of determining the quality detection result of the localization map based on the iteration number corresponding to the initial pose information and the difference value between the initial pose information and the first pose information includes: determining the quality detection result of the localization map as a first result based on the iteration number being not less than a threshold and the difference value between the initial pose information and the first pose information being greater than a difference threshold. Determining the quality detection result of the localization map as a second result based on the iteration number being less than the threshold and / or the difference value between the initial pose information and the first pose information being not greater than a difference threshold. The difference threshold can be set based on experience or adjusted according to the implementation environment; this embodiment does not limit this.

[0091] This application does not limit the process of determining the difference between the initial attitude information and the first attitude information. Optionally, the difference between the initial attitude information and the first attitude information in each dimension is determined; based on the difference between the initial attitude information and the first attitude information in each dimension, the difference between the initial attitude information and the first attitude information is determined. For example, the sum of the difference values ​​between the initial attitude information and the first attitude information in each dimension is used as the difference between the initial attitude information and the first attitude information. Alternatively, the maximum value of the difference values ​​between the initial attitude information and the first attitude information in each dimension is used as the difference between the initial attitude information and the first attitude information. Or, the average value of the difference values ​​between the initial attitude information and the first attitude information in each dimension is used as the difference between the initial attitude information and the first attitude information.

[0092] Optionally, a first region can be determined based on the location included in the initial attitude information. A second region can be determined based on the location included in the first attitude information. The overlap between the first and second regions can be determined. The quality detection result of the localization map can be determined based on the number of iterations, the difference between the initial attitude information and the first attitude information, and the overlap between the first and second regions. This application embodiment does not limit the order of the processes for determining the number of iterations, determining the difference between the initial attitude information and the first attitude information, and determining the overlap between the first and second regions.

[0093] The process of determining the first region based on the position included in the initial attitude information includes: using the position included in the initial attitude information as a reference point and the target length as a reference distance to determine the first region. The target length can be set based on experience or adjusted according to the implementation environment; this embodiment does not limit this. For example, the target length is 5 meters.

[0094] For example, a circle is defined with the position included in the initial attitude information as the center and the target length as the radius, and the area covered by this circle is taken as the first region. Figure 3 The image shown is a schematic diagram illustrating the display of a first region according to an embodiment of this application. Figure 3 In the diagram, point A represents the location included in the initial pose information, and the shaded area is the first region.

[0095] For example, using the position included in the initial attitude information as the center and the target length as the variable length, a square is defined, and the area covered by this square is taken as the first region. Figure 4 The diagram shown is a schematic representation of another first region provided in an embodiment of this application. Figure 4 In the diagram, point A represents the location included in the initial pose information, and the shaded area is the first region.

[0096] It should be noted that the process of determining the second region based on the location included in the first attitude information is similar to the process of determining the first region based on the location included in the initial attitude information, and will not be repeated here.

[0097] After determining the first region and the second region, the region that belongs to both the first region and the second region is considered the overlapping region. If the first region and the second region do not overlap after determination, the first region and / or the second region are re-determined until the re-determined two regions overlap. The second region and the first region may have the same or different shapes, the same or different areas, and the same or different perimeters. This application does not impose any limitations on these aspects.

[0098] like Figure 5 The diagram shown is a schematic representation of the overlapping area between a first region and a second region according to an embodiment of this application. Figure 5 In the diagram, point A represents the location included in the initial attitude information, the area covered by circle one is the first region, point B represents the location included in the first attitude information, the area covered by circle two is the second region, and the shaded area is the overlapping area between the first and second regions.

[0099] In one possible implementation, the process of determining the overlap between the first region and the second region is not limited. Optionally, the area of ​​the overlapping region between the first region and the second region is determined; the area of ​​the first region is determined, and the quotient between the area of ​​the overlapping region and the area of ​​the first region is used as the overlap between the first region and the second region. Since the areas of the first region and the second region are the same, the area of ​​the second region can also be determined, and the quotient between the area of ​​the overlapping region and the area of ​​the second region can be used as the overlap between the first region and the second region.

[0100] In one possible implementation, the process of determining the quality inspection result of the localization map based on the number of iterations, the difference between the initial pose information and the first pose information, and the overlap between the first region and the second region includes: determining the quality inspection result of the localization map as a first result based on the number of iterations not being less than a threshold, the difference between the initial pose information and the first pose information being greater than a difference threshold, and the overlap between the first region and the second region being less than an overlap threshold. Determining the quality inspection result of the localization map as a second result based on the number of iterations being less than a threshold, and / or the difference between the initial pose information and the first pose information not being greater than a difference threshold, and / or the overlap between the first region and the second region not being less than an overlap threshold. The overlap threshold can be set based on experience or adjusted according to the implementation environment; this embodiment does not limit this. For example, the overlap threshold is 80%.

[0101] The above method determines the number of iterations corresponding to the initial attitude information of the autonomous vehicle at the target time in real time during the driving process, and then determines the quality detection result of the localization map based on the number of iterations. This method detects the quality of the localization map during the driving process of the autonomous vehicle, advances the timing of the localization map quality detection, reduces the occurrence of failures when the autonomous vehicle drives according to the localization map, and further improves the safety of the autonomous vehicle driving according to the localization map.

[0102] Furthermore, if the number of iterations of the initial attitude information at the target time is too high, it indicates that there is a large difference between the actual environmental information of the location included in the initial attitude information and the environmental information of that location in the positioning map. This can prompt the user to promptly correct the environmental information in the positioning map, making the environmental information in the positioning map more consistent with the actual environmental information, thereby improving the accuracy of the positioning map and further enhancing the safety and positioning accuracy of autonomous vehicles driving according to the positioning map.

[0103] Figure 6 The diagram shown is a structural schematic of a location map quality inspection device provided in an embodiment of this application. Figure 6 As shown, the device includes:

[0104] The acquisition module 601 is used to acquire the initial attitude information and target point cloud data of the autonomous vehicle at the target time. The target point cloud data is the point cloud data obtained by the autonomous vehicle emitting laser pulses to the target object according to the initial attitude information at the target time. The target point cloud data includes the feature information of multiple feature points.

[0105] The determination module 602 is used to iteratively update the initial attitude information based on the positioning map and the feature information of multiple feature points to obtain the number of iterations of the initial attitude information. The number of iterations refers to the number of times when the attitude information obtained by iteratively updating the initial attitude information meets the update requirements.

[0106] The determination module 602 is also used to determine the quality inspection result of the positioning map based on the number of iterations. The quality inspection result is used to indicate whether the environmental information in the positioning map needs to be repaired.

[0107] In one possible implementation, the determining module 602 is used to determine reference attitude information based on the positioning map and the feature information of multiple feature points. Target point cloud data can be obtained by emitting laser pulses at the target object according to the reference attitude information in the positioning map. The initial attitude information is iteratively updated based on the feature information of multiple feature points to obtain first attitude information. The relationship between the first attitude information and the reference attitude satisfies the relationship requirements. The iteration number when the first attitude information is obtained is taken as the iteration number of the initial attitude information.

[0108] In one possible implementation, the determining module 602 is used to acquire multiple candidate pose information; determine the point cloud data when laser pulses are emitted to the target object according to each candidate pose information in the positioning map, and obtain the candidate point cloud data corresponding to each candidate pose information, wherein each candidate point cloud data includes feature information of multiple feature points; and determine reference pose information based on the feature information of multiple feature points included in the target point cloud data and the feature information of multiple feature points included in each candidate point cloud data.

[0109] In one possible implementation, the determining module 602 is further configured to call a loss function to determine the loss value between the first attitude information and the reference attitude information; based on the loss value between the first attitude information and the reference attitude information being less than a loss threshold, determine that the relationship between the first attitude information and the reference attitude information meets the relationship requirements; based on the loss value between the first attitude information and the reference attitude information not being less than the loss threshold, determine that the relationship between the first attitude information and the reference attitude information does not meet the relationship requirements.

[0110] In one possible implementation, the determining module 602 is used to determine the quality detection result of the positioning map as a first result based on the number of iterations not being less than a threshold number, the first result being used to indicate that the environmental information in the positioning map needs to be repaired; and to determine the quality detection result of the positioning map as a second result based on the number of iterations being less than the threshold number, the second result being used to indicate that the environmental information in the positioning map does not need to be repaired.

[0111] In one possible implementation, the determining module 602 is also used to determine the target location in the positioning map where the environmental information needs to be repaired;

[0112] The device also includes:

[0113] The display module is used to display notification messages, which instruct the repair of environmental information for the target location in the positioning map.

[0114] In one possible implementation, the determining module 602 is used to determine the location included in the initial attitude information in the positioning map as the target location where the environmental information in the positioning map needs to be repaired.

[0115] The aforementioned device determines the number of iterations of the initial attitude information of the autonomous vehicle at the target time in real time during the driving process, and then determines the quality inspection result of the positioning map based on the number of iterations. This advances the timing of the positioning map quality inspection, reduces the possibility of malfunctions when the autonomous vehicle is driving according to the positioning map, and further improves the safety of the autonomous vehicle driving according to the positioning map.

[0116] It should be understood that the above-described apparatus is only illustrated by the division of the functional modules described above when implementing its functions. In practical applications, the functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0117] Figure 7 A structural block diagram of a terminal device 700 provided in an exemplary embodiment of this application is shown. The terminal device 700 may be a portable mobile terminal, such as a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The terminal device 700 may also be referred to as a user device, portable terminal, laptop terminal, desktop terminal, or other names.

[0118] Typically, terminal device 700 includes a processor 701 and a memory 702.

[0119] Processor 701 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 701 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 701 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 701 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 701 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0120] The memory 702 may include one or more computer-readable storage media, which may be non-transitory. The memory 702 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 702 is used to store at least one instruction, which is executed by the processor 701 to implement the quality detection method for the positioning map provided in the method embodiments of this application.

[0121] In some embodiments, the terminal device 700 may also optionally include a peripheral device interface 703 and at least one peripheral device. The processor 701, memory 702, and peripheral device interface 703 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 703 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 704, a display screen 705, a camera assembly 706, an audio circuit 707, a positioning assembly 708, and a power supply 709.

[0122] Peripheral device interface 703 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 701 and memory 702. In some embodiments, processor 701, memory 702 and peripheral device interface 703 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 701, memory 702 and peripheral device interface 703 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0123] The radio frequency (RF) circuit 704 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 704 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 704 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 704 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 704 can communicate with other terminal devices through at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 704 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0124] Display screen 705 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 705 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 701 for processing. In this case, display screen 705 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, display screen 705 may be a single screen, disposed on the front panel of terminal device 700; in other embodiments, display screen 705 may be at least two screens, disposed on different surfaces of terminal device 700 or in a folded design; in still other embodiments, display screen 705 may be a flexible display screen, disposed on a curved or folded surface of terminal device 700. Furthermore, display screen 705 may be configured as a non-rectangular irregular shape, i.e., a non-rectangular screen. Display screen 705 may be made of materials such as LCD (Liquid Crystal Display) or OLED (Organic Light-Emitting Diode).

[0125] The camera assembly 706 is used to acquire images or videos. Optionally, the camera assembly 706 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the terminal device 700, and the rear-facing camera is located on the back of the terminal device 700. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 706 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cool light flash, which can be used for light compensation at different color temperatures.

[0126] The audio circuit 707 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 701 for processing, or input to the radio frequency circuit 704 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each located at a different part of the terminal device 700. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert the electrical signals from the processor 701 or the radio frequency circuit 704 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 707 may also include a headphone jack.

[0127] The positioning component 708 is used to locate the current geographical location of the terminal device 700 in order to enable navigation or LBS (Location Based Service). The positioning component 708 can be a positioning component based on the US GPS (Global Positioning System), China's BeiDou system, Russia's Granas system, or the EU's Galileo system.

[0128] Power supply 709 is used to supply power to the various components in terminal device 700. Power supply 709 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 709 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, and a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0129] In some embodiments, the terminal device 700 further includes one or more sensors 710. The one or more sensors 710 include, but are not limited to: an accelerometer 711, a gyroscope 712, a pressure sensor 713, a fingerprint sensor 714, an optical sensor 715, and a proximity sensor 716.

[0130] Accelerometer 711 can detect the magnitude of acceleration along the three coordinate axes of a coordinate system established by terminal device 700. For example, accelerometer 711 can be used to detect the components of gravitational acceleration along the three coordinate axes. Processor 701 can control display screen 705 to display the user interface in either a landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 711. Accelerometer 711 can also be used for games or for acquiring user motion data.

[0131] The gyroscope sensor 712 can detect the orientation and rotation angle of the terminal device 700. The gyroscope sensor 712, in conjunction with the accelerometer sensor 711, can collect 3D motion data from the user on the terminal device 700. Based on the data collected by the gyroscope sensor 712, the processor 701 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0132] The pressure sensor 713 can be disposed on the side bezel of the terminal device 700 and / or on the lower layer of the display screen 705. When the pressure sensor 713 is disposed on the side bezel of the terminal device 700, it can detect the user's grip signal on the terminal device 700, and the processor 701 can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor 713. When the pressure sensor 713 is disposed on the lower layer of the display screen 705, the processor 701 can control the operable controls on the UI interface based on the user's pressure operation on the display screen 705. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0133] The fingerprint sensor 714 is used to collect a user's fingerprint. The processor 701 identifies the user based on the fingerprint collected by the fingerprint sensor 714, or vice versa. When the user's identity is identified as trusted, the processor 701 authorizes the user to perform relevant sensitive operations, including unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings. The fingerprint sensor 714 can be located on the front, back, or side of the terminal device 700. When the terminal device 700 has a physical button or manufacturer logo, the fingerprint sensor 714 can be integrated with the physical button or manufacturer logo.

[0134] An optical sensor 715 is used to collect ambient light intensity. In one embodiment, the processor 701 can control the display brightness of the display screen 705 based on the ambient light intensity collected by the optical sensor 715. Specifically, when the ambient light intensity is high, the display brightness of the display screen 705 is increased; when the ambient light intensity is low, the display brightness of the display screen 705 is decreased. In another embodiment, the processor 701 can also dynamically adjust the shooting parameters of the camera assembly 706 based on the ambient light intensity collected by the optical sensor 715.

[0135] The proximity sensor 716, also known as a distance sensor, is typically located on the front panel of the terminal device 700. The proximity sensor 716 is used to detect the distance between the user and the front of the terminal device 700. In one embodiment, when the proximity sensor 716 detects that the distance between the user and the front of the terminal device 700 is gradually decreasing, the processor 701 controls the display screen 705 to switch from a screen-on state to a screen-off state; when the proximity sensor 716 detects that the distance between the user and the front of the terminal device 700 is gradually increasing, the processor 701 controls the display screen 705 to switch from a screen-off state to a screen-on state.

[0136] Those skilled in the art will understand that Figure 7 The structure shown does not constitute a limitation on the terminal device 700, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0137] Figure 8 This is a schematic diagram of the server structure provided in the embodiments of this application. The server 800 can vary considerably due to different configurations or performance. It may include one or more Central Processing Units (CPUs) 801 and one or more memories 802. The one or more memories 802 store at least one line of program code, which is loaded and executed by the one or more processors 801 to implement the location map quality detection method provided in the various method embodiments described above. Of course, the server 800 may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The server 800 may also include other components for implementing device functions, which will not be elaborated here.

[0138] In an exemplary embodiment, a computer-readable storage medium is also provided, which stores at least one piece of program code that is loaded and executed by a processor to enable a computer to implement any of the above-described methods for quality detection of positioning maps.

[0139] Optionally, the aforementioned computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0140] In an exemplary embodiment, a computer program or computer program product is also provided, which stores at least one computer instruction, which is loaded and executed by a processor to enable the computer to implement any of the above-described methods for quality detection of positioning maps.

[0141] It should be noted that all information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the positioning map, attitude information, and point cloud data involved in this application were all obtained with full authorization.

[0142] It should be understood that "multiple" as used in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0143] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0144] The above description is merely an exemplary embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for quality inspection of a positioning map, characterized in that, The method includes: The initial attitude information and target point cloud data of the autonomous vehicle at the target time are obtained. The target point cloud data is the point cloud data obtained by the autonomous vehicle emitting laser pulses to the target object according to the initial attitude information at the target time. The target point cloud data includes feature information of multiple feature points. Based on the positioning map and the feature information of the multiple feature points, the initial attitude information is iteratively updated to obtain the number of iterations of the initial attitude information. The number of iterations refers to the number of times when the attitude information obtained by iteratively updating the initial attitude information meets the update requirements. Based on the number of iterations, the quality inspection result of the positioning map is determined, and the quality inspection result is used to indicate whether the environmental information in the positioning map needs to be repaired; The step of iteratively updating the initial pose information based on the positioning map and the feature information of the multiple feature points to obtain the number of iterations of the initial pose information includes: Based on the positioning map and the feature information of the multiple feature points, a reference attitude information is determined. By emitting a laser pulse toward the target object in the positioning map according to the reference attitude information, the target point cloud data can be obtained. Based on the feature information of the multiple feature points, the initial pose information is iteratively updated to obtain the first pose information, and the relationship between the first pose information and the reference pose satisfies the relationship requirements. The number of iterations when the first attitude information is obtained is taken as the number of iterations for the initial attitude information; The method further includes: The loss function is invoked to determine the loss value between the first attitude information and the reference attitude information; Based on the fact that the loss value between the first attitude information and the reference attitude information is less than the loss threshold, it is determined that the relationship between the first attitude information and the reference attitude information satisfies the relationship requirement; Based on the fact that the loss value between the first attitude information and the reference attitude information is not less than the loss threshold, it is determined that the relationship between the first attitude information and the reference attitude information does not meet the relationship requirement.

2. The method according to claim 1, characterized in that, The step of determining the reference pose information based on the positioning map and the feature information of the multiple feature points includes: Obtain multiple candidate pose information; The point cloud data when laser pulses are emitted toward the target object according to each candidate posture information in the positioning map is determined, and the candidate point cloud data corresponding to each candidate posture information is obtained. Each candidate point cloud data includes feature information of multiple feature points. The reference pose information is determined based on the feature information of multiple feature points included in the target point cloud data and the feature information of multiple feature points included in each candidate point cloud data.

3. The method according to claim 1 or 2, characterized in that, Determining the quality inspection result of the positioning map based on the number of iterations includes: Based on the fact that the number of iterations is not less than the number threshold, the quality detection result of the positioning map is determined as the first result, and the first result is used to indicate that the environmental information in the positioning map needs to be repaired. Based on the fact that the number of iterations is less than the threshold number, the quality detection result of the positioning map is determined as the second result, which is used to indicate that the environmental information in the positioning map does not need to be repaired.

4. The method according to claim 3, characterized in that, After determining the quality detection result of the positioning map as the first result based on the fact that the number of iterations is not less than a threshold, the method further includes: Determine the target locations in the positioning map where environmental information needs to be repaired; Display a notification message, which instructs that the environmental information of the target location in the positioning map be repaired.

5. The method according to claim 4, characterized in that, Determining the target location in the positioning map where environmental information needs to be repaired includes: The location included in the initial attitude information in the positioning map is used as the target location where the environmental information in the positioning map needs to be repaired.

6. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing at least one piece of program code, which is loaded and executed by the processor to enable the electronic device to implement the quality detection method for the positioning map as described in any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one piece of program code, which is loaded and executed by a processor to enable the computer to implement the quality detection method for the positioning map as described in any one of claims 1 to 5.

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