Map updating method, electronic device, and storage medium

Abstract

Embodiments of the present application provide a map updating method, an electronic device and a storage medium. The method comprises: acquiring lateral deviation data of each movable device at each front node in a topological map; determining whether to update global pose information of the front node and a target node corresponding to the front node according to statistical information of the lateral deviation data of each movable device at the same front node; if it is determined to update the global pose information of the front node and the target node, determining target global pose information of the front node and target global pose information of the target node according to pose information of each movable device at the front node; and updating, in the topological map, the global pose information of the front node to the target global pose information and updating the global pose information of the target node to the target global pose information. Embodiments of the present application realize automatic updating of the topological map, and can reduce maintenance cost.

Description

Technical Field

[0001] This application relates to the field of positioning technology, and in particular to a map updating method, electronic device, and storage medium. Background Technology

[0002] With the advancement of technology, mobile device technology (such as mobile robots) has developed rapidly. Many point-to-point handling tasks in warehouses or factories have shifted from humans to mobile devices. SLAM (Simultaneous Localization and Mapping) mobile devices have been favored by many factories due to their ease of deployment and low maintenance costs. Differential wheel mobile devices are the first choice for mobile devices due to their high speed, high efficiency, and low cost.

[0003] Once SLAM deployment is complete for a mobile device, the positioning map and its trajectory (topology map) are fixed. However, at some high-precision points, the lateral deviation of the preceding point must not be too large. For example, when docking with a machine, if the lateral deviation between the preceding point and the machine's interface is too large, the mobile device will not be able to adjust its pose upon reaching the machine. The main causes and solutions for this problem of large preceding point errors include: issues related to the accuracy of the marking points. During marking, due to operational reasons or the limitations of the mobile device itself, the lateral error cannot be completely eliminated, sometimes resulting in a deviation of several centimeters. This inherent deviation will cause the mobile device to shift in one direction. Environmental changes can also cause instability in the pose upon reaching the preceding point. Combined with the increased marking accuracy, this may exceed a threshold, making it difficult for the mobile device to adjust its pose upon reaching the high-precision point.

[0004] In existing technologies, when a high-precision point is found to have poor accuracy and this problem occurs repeatedly, points (including high-precision points and previous points) are re-marked. If most points in a certain area are also inaccurate, the environmental map is updated, and the updated portion requires re-marking or adjusting points. Because this method cannot effectively and quantitatively evaluate the accuracy of its previous points, the re-marked points may not be optimal, and the environmental map needs to be re-marked after updates. Summary of the Invention

[0005] In view of the above problems, embodiments of this application are proposed to provide a map updating method, electronic device, and storage medium that overcomes or at least partially solves the above problems.

[0006] According to a first aspect of the embodiments of this application, a map updating method is provided, comprising:

[0007] The lateral deviation data of each mobile device at each preceding node in the topology map is obtained. The preceding node is a node that is adjacent to the target node and located before the target node in the topology map. The topology map represents the running path of the mobile device. The lateral deviation data is the lateral deviation between the pose information of the mobile device at the preceding node and the reference pose information.

[0008] Based on the statistical information of the lateral deviation data corresponding to the same front node for each mobile device, determine whether to update the global pose information of the front node and the target node corresponding to the front node.

[0009] If it is determined that the global pose information of the front node and the target node should be updated, the target global pose information of the front node and the target global pose information of the target node are determined according to the pose information of each mobile device at the front node. The global pose information of the front node represents the global reference pose information of the mobile device at the front node, and the global pose information of the target node represents the global reference pose information of the mobile device at the target node.

[0010] In the topology map, the global pose information of the preceding node is updated to the target global pose information, and the global pose information of the target node is updated to the target global pose information.

[0011] According to a second aspect of the embodiments of this application, an electronic device is provided, including: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the map update method as described in the first aspect.

[0012] According to a third aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, and when executed by a processor, the computer program implements the map update method as described in the first aspect.

[0013] According to a fourth aspect of the embodiments of this application, a computer program product is provided, including a computer program or computer instructions, which, when executed by a processor, implement the map update method described in the first aspect.

[0014] The map update method, electronic device, and storage medium provided in this application obtain lateral deviation data of each mobile device at each preceding node in the topology map. Based on the statistical information of the lateral deviation data of each mobile device at the same preceding node, it determines whether to update the global pose information of the preceding node and the target node corresponding to the preceding node. If it is determined that the global pose information of the preceding node and the target node should be updated, the target global pose information of the preceding node and the target global pose information of the target node are determined based on the pose information of each mobile device at the preceding node. In the topology map, the global pose information of the preceding node is updated to the target global pose information of the preceding node, and the global pose information of the target node is updated to the target global pose information of the target node. Since the preceding node and the target node whose global pose information needs to be updated can be determined based on the lateral deviation data of the mobile device passing through the preceding node, and then the global pose information of the preceding node and the target node is updated, the topology map is automatically updated without re-marking points, which can reduce maintenance costs and improve the efficiency of mobile device tasks.

[0015] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application.

[0017] Figure 1 This is a flowchart illustrating the steps of a map updating method provided in an embodiment of this application;

[0018] Figure 2 This is a schematic diagram illustrating the calculation of lateral deviation data in an embodiment of this application;

[0019] Figure 3 This is a flowchart of another map updating method provided in an embodiment of this application;

[0020] Figure 4a This is an example image of the location area to be updated in the location map in an embodiment of this application;

[0021] Figure 4b Yes Figure 4a An example diagram of the factor map for frame pose optimization of the localization region shown.

[0022] Figure 5This is a structural block diagram of a map updating device provided in an embodiment of this application;

[0023] Figure 6 This is a structural block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0024] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0025] In recent years, significant progress has been made in research on technologies based on artificial intelligence, such as computer vision, deep learning, machine learning, image processing, and image recognition. Artificial intelligence (AI) is an emerging science and technology that studies and develops theories, methods, technologies, and application systems to simulate and extend human intelligence. AI is a comprehensive discipline involving numerous technologies, including chips, big data, cloud computing, the Internet of Things, distributed storage, deep learning, machine learning, and neural networks. Computer vision, as an important branch of AI, specifically enables machines to recognize the world. Computer vision technologies typically include face recognition, liveness detection, fingerprint recognition and anti-counterfeiting verification, biometric recognition, face detection, pedestrian detection, object detection, image processing, image recognition, image semantic understanding, image retrieval, text recognition, video processing, video content recognition, 3D reconstruction, virtual reality, augmented reality, simultaneous localization and mapping (SLAM), computational photography, and robot navigation and localization. With the research and advancement of artificial intelligence technology, this technology has been applied in numerous fields, such as security and prevention, urban management, traffic management, building management, park management, facial recognition access control, facial recognition attendance, logistics management, warehouse management, robotics, intelligent marketing, computational photography, mobile imaging, cloud services, smart homes, wearable devices, autonomous driving, smart healthcare, facial payment, facial unlocking, fingerprint unlocking, identity verification, smart screens, smart TVs, cameras, mobile internet, live streaming, beautification, makeup, medical aesthetics, and intelligent temperature measurement. This application also relates to computer vision technology, specifically a map updating method for updating maps on mobile devices. The specific solution is as follows:

[0026] Figure 1 This is a flowchart illustrating the steps of a map update method provided in this application embodiment. This method can be applied to update maps on mobile devices (such as mobile robots) and can be executed by electronic devices such as servers corresponding to the mobile devices. Figure 1 As shown, the method may include:

[0027] Step 101: Obtain the lateral deviation data of each mobile device at each preceding node in the topology map. The preceding node is a node in the topology map that is adjacent to the target node and located before the target node.

[0028] A topological map, a type of statistical map in cartography, is an abstract map that maintains the correct relative positions of points and lines but not necessarily the correct shape, area, distance, or direction. It represents the operating path of a mobile device. A topological map represents an indoor environment as a topological structure diagram with nodes and connecting lines. Nodes represent important locations in the environment (such as corners, doors, elevators, stairs, etc.), and edges represent the connections between nodes, such as corridors. The target node is the destination point that the mobile device needs to reach, such as the location of a shelf. The mobile device is a differential speed model.

[0029] The lateral deviation data is the lateral deviation between the pose information (actual pose information) of the mobile device at the front node and the reference pose information. Figure 2 This is a schematic diagram illustrating the calculation of lateral deviation data in an embodiment of this application, as shown below. Figure 2 As shown, 1 represents the reference pose information of the mobile device at the target node, 2 represents the reference pose information of the mobile device at the front node when there is no error, and 3 represents the actual pose information of the mobile device. It can be seen that the actual pose information of the mobile device has a lateral deviation 4 relative to the reference pose information at the front node. Since the mobile device is a differential speed model, it cannot eliminate the accumulated error by lateral movement, and the movement process cannot produce large twisting, because large twisting may cause the mobile device to collide with the black obstacle 5.

[0030] For such high-precision tasks, relative positioning based on a reference point (mark) (including laser or visual reference points) is typically used. This positioning method reports the pose information of the mobile device to the reference point in real time, and navigation plans accordingly based on this data. Ordinary positioning cannot guarantee its accuracy; therefore, this embodiment uses relative positioning as the reference. The reference point is an identifier point corresponding to the target node and is also the origin of the coordinate system for determining pose information (including the pose information of the preceding node, the target node, and the mobile device). For example, when the target node corresponds to a U-shaped groove, the reference point is the center point of the back of the U-shaped groove.

[0031] In one embodiment of this application, obtaining the lateral deviation data of each mobile device at each preceding node in the topology map includes:

[0032] Receive lateral deviation data reported by each of the mobile devices at each of the front-end nodes; or

[0033] The system receives pose information of each mobile device relative to a reference point, and determines the lateral deviation data of the mobile device at the preceding node corresponding to the reference point based on the pose information. The reference point is the origin of the coordinate system used to determine the pose information.

[0034] When a mobile device reaches a preceding node corresponding to a target node, it can calculate the lateral deviation data relative to that preceding node and report this lateral deviation data to the server. The server receives the lateral deviation data reported by the mobile device. Alternatively, when a mobile device reaches a preceding node corresponding to a target node, it can report its pose information relative to a reference point (Mark) to the server. The server receives this pose information and, based on the pose information of the mobile device relative to the reference point and the pose information of the target node relative to the reference point, calculates the pose information of the mobile device relative to the target node, and extracts the lateral deviation data from this pose information. Specifically, the pose information of the mobile device relative to the target node is calculated using the following formula:

[0035]

[0036] in, This represents the pose information of the mobile device relative to the target node. This represents the pose information of the target node relative to the reference point. This represents the pose information of the mobile device relative to the reference point.

[0037] After obtaining the lateral deviation data of the mobile device, the lateral deviation data can be recorded, including the number of times, mean, and variance of the lateral deviation of each mobile device at each forward point.

[0038] In one embodiment of this application, after obtaining the lateral deviation data of each mobile device at each preceding node in the topology map, the method further includes: for each mobile device, recording the number, mean, and variance of the lateral deviation data generated by the mobile device at each preceding node; and for each preceding node, recording the number, mean, and variance of the lateral deviation generated by each mobile device at the preceding node.

[0039] After acquiring the lateral deviation data of the mobile device at the preceding node, record the number of times n, the mean u, and the variance σ of the lateral deviation data generated by the mobile device at the preceding node, as well as the accuracy at which it reaches the target node (i.e., the high-precision point, requiring high accuracy). The data format for recording the lateral deviation data corresponding to the mobile device is map. <robot_id,shard_ptr1 <msgs>>, where robot_id represents the identifier of the removable device, shard_ptr1 <msgs>This represents the frequency, mean, and variance of the lateral deviation data generated by the mobile device at each preceding node. The data format for recording the lateral deviation data corresponding to the preceding node is a map. <node_id,shard_ptr2 <msgs>>, where node_id represents the identifier of the preceding node, and shard_ptr2<Msgs> represents the number, mean, and variance of the lateral deviations generated by each mobile device at that preceding node.

[0040] When acquiring lateral deviation data, the recorded data is updated. It is not necessary to retain all previous lateral deviation data. Iterative calculations can be performed based on the current observation data, using the following formula:

[0041] n new =n+1,

[0042]

[0043]

[0044] Where, n new The number of lateral deviation data points after iteration is represented by , and 'n' represents the number of lateral deviation data points before iteration. new Let u represent the mean of the lateral deviation data after iteration, and o represent the mean of the lateral deviation data before iteration. detect σ represents the lateral deviation data in this observation data. new σ represents the variance of the lateral deviation data after iteration, and σ represents the variance of the lateral deviation data before iteration. now This indicates the difference between the lateral deviation data and the mean u. new The variance of the lateral deviation data obtained in this study.

[0045] By recording the lateral deviation data generated by the mobile device at each preceding node, and the lateral deviation data generated by each mobile device at the preceding node, when the number of lateral deviation data reaches a certain number, the recorded data (mean, variance) can be directly used to judge the lateral deviation data and determine the map data to be updated.

[0046] Step 102: Based on the statistical information of the lateral deviation data corresponding to the same front node for each mobile device, determine whether to update the global pose information of the front node and the target node corresponding to the front node.

[0047] For each foreground node, the lateral deviation data generated by each mobile device at that foreground node is statistically analyzed to obtain statistical information on the lateral deviation data corresponding to each foreground node. It is then determined whether the statistical information on the lateral deviation data corresponding to each foreground node meets the target conditions. If the statistical information on the lateral deviation data corresponding to a foreground node meets the target conditions, it is determined that the global pose information of that foreground node and its corresponding target node should be updated; if the statistical information on the lateral deviation data corresponding to a foreground node does not meet the target conditions, it is determined that the global pose information of that foreground node and its corresponding target node does not need to be updated.

[0048] Step 103: If it is determined that the global pose information of the front node and the target node needs to be updated, the target global pose information of the front node and the target global pose information of the target node are determined based on the pose information of each mobile device at the front node.

[0049] The global pose information of the front node represents the global reference pose information of the mobile device at the front node, and the global pose information of the target node represents the global reference pose information of the mobile device at the target node.

[0050] When updating the global pose information of a preceding node and its corresponding target node, the global pose information of the preceding node can be corrected based on the pose information of each mobile device at that preceding node, combined with the pose information of both the preceding and target nodes, to obtain the target global pose information of that preceding node. Similarly, the global pose information of the target node can be corrected to obtain the target global pose information of the target node. The pose information of the mobile device at the preceding node can include the global pose information of the mobile device and the pose information of the reference node relative to the mobile device. The global pose information is relative to the origin of the target coordinate system, which is also the origin of the localization map.

[0051] In one embodiment of this application, determining the target global pose information of the front-end node and the target global pose information of the target node based on the pose information of each mobile device at the front-end node includes:

[0052] For each of the mobile devices, the global pose information of the target node and the global pose information of the front node are determined based on the pose information of the target node relative to the reference point, the pose information of the front node relative to the target node, the global pose information of the mobile device, and the pose information of the reference point relative to the mobile device. The reference point is the origin of the coordinate system for determining the pose information of the mobile device.

[0053] The target global pose information of the target node is obtained by averaging the global pose information of the target node determined under each of the mobile devices, and the target global pose information of the front node is obtained by averaging the global pose information of the front node determined under each of the mobile devices.

[0054] One target node corresponds to one reference point. The pose information of the mobile device at the front node is the pose information of the mobile device at the front node relative to the reference point.

[0055] After determining that the global pose information of the preceding node and the target node needs to be updated, the pose information of the target node relative to the reference point and the pose information of the preceding node relative to the target node can be obtained from the topology map. Furthermore, the global pose information of the mobile device and the pose information of the reference point relative to the mobile device (i.e., the pose information of the reference point in the mobile device's coordinate system) can be obtained from the data reported by the mobile device. For each mobile device, the product of the global pose information of the mobile device, the pose information of the reference point relative to the mobile device, and the pose information of the target node relative to the target reference point can be determined, and this product is used as the global pose information of the target node. Similarly, the product of the global pose information of the mobile device, the pose information of the reference point relative to the mobile device, the pose information of the target node relative to the reference point, and the pose information of the preceding node relative to the target node can be determined, and this product is used as the global pose information of the preceding node. In other words, the global pose information of the target node is determined using the following formula:

[0056]

[0057] in, This represents the global pose information of the target node. Represents the global pose information of the mobile device. This represents the pose information of the reference point relative to the mobile device. This represents the pose information of the target node relative to the reference point.

[0058] The global pose information of the preceding node is determined by the following formula:

[0059]

[0060] in, This represents the global pose information of the preceding node. Represents the global pose information of the mobile device. This represents the pose information of the reference point relative to the mobile device. This represents the pose information of the target node relative to the reference point. This represents the pose information of the preceding node relative to the target node. Each global pose information is determined relative to the origin of the localization map.

[0061] The pose information of the target node and the preceding node obtained above are generated by a single mobile device. The average of the global pose information of the target node determined across all mobile devices is used as the target global pose information of the target node. Similarly, the average of the global pose information of the preceding node determined across all mobile devices is used to obtain the target global pose information of the preceding node. This achieves the updating of the global pose information of the preceding and target nodes based on the pose information of each mobile device. The global pose information includes position information and attitude information (angle). When averaging the global position information across all mobile devices, the position information and attitude information are averaged separately.

[0062] In one embodiment of this application, the step of calculating the average of the global pose information of the target nodes determined under each of the mobile devices to obtain the target global pose information of the target nodes includes: removing the maximum and minimum global pose information from the global pose information of the target nodes determined under each of the mobile devices, and determining the average of the remaining global pose information as the target global pose information of the target nodes.

[0063] The step of calculating the average of the global pose information of the front node determined under each of the mobile devices to obtain the target global pose information of the front node includes: removing the maximum and minimum global pose information from the global pose information of the front node determined under each of the mobile devices, and determining the average of the remaining global pose information as the target global pose information of the front node.

[0064] When calculating the average global pose information of the target node determined under each mobile device, the maximum and minimum global pose information can be removed, and the average of the remaining global pose information can be calculated. This average value is then determined as the target global pose information of the target node. Similarly, when calculating the average global pose information of the front node determined under multiple mobile devices, the maximum and minimum global pose information can be removed, and the average of the remaining global pose information can be calculated. This average value is then determined as the target global pose information of the front node.

[0065] Since the maximum and minimum global pose information have large errors, removing them and then calculating the average can improve the accuracy of the global pose information of the determined target node and the preceding node, and enhance robustness.

[0066] Step 104: In the topology map, update the global pose information of the preceding node to the target global pose information, and update the global pose information of the target node to the target global pose information.

[0067] In the topology map, the original global pose information of the preceding node is updated to the target global pose information, and the original global pose information of the target node is updated to the target global pose information, thus completing the update of the global pose information of the preceding node and the target node.

[0068] When updating the map, in order to indicate to the operator that the system is running and working, the preceding nodes that may need to be updated can be displayed differently in the original topology map (for example, the preceding nodes to be updated can be displayed in red), so as to remind the operator of the preceding nodes to be updated.

[0069] The map update method provided in this embodiment obtains the lateral deviation data of each mobile device at each preceding node in the topology map. Based on the statistical information of the lateral deviation data of each mobile device at the same preceding node, it determines whether to update the global pose information of the preceding node and the target node corresponding to the preceding node. If it is determined that the global pose information of the preceding node and the target node should be updated, the target global pose information of the preceding node and the target global pose information of the target node are determined based on the pose information of each mobile device at the preceding node. In the topology map, the global pose information of the preceding node is updated to the target global pose information of the preceding node, and the global pose information of the target node is updated to the target global pose information of the target node. Since the preceding node and the target node whose global pose information needs to be updated can be determined based on the lateral deviation data of the mobile device passing through the preceding node, and then the global pose information of the preceding node and the target node is updated, the topology map is automatically updated without re-marking points, which can reduce maintenance costs and improve the efficiency of mobile device tasks.

[0070] Based on the above technical solution, before determining whether to update the global pose information of the front node and the target node corresponding to the front node according to the statistical information of the lateral deviation data corresponding to each mobile device at the same front node, the method further includes: for each front point, determining the statistical number of mobile devices whose lateral deviation data is greater than a first deviation threshold at the front point node, as the statistical information corresponding to the front node.

[0071] For each foreground node, the lateral deviation data generated by each mobile device at that foreground node is compared with the deviation threshold. The number of mobile devices with lateral deviation data exceeding the deviation threshold is then counted. This count represents the statistical information for that foreground node. The statistical information of a foreground node can be used as a basis for determining whether that foreground node needs to update its global pose information.

[0072] In one embodiment of this application, determining whether to update the global pose information of the front node and the target node corresponding to the front node based on the statistical information of the lateral deviation data corresponding to each mobile device at the same front node includes: determining a first ratio between the statistical number and the total number of mobile devices corresponding to the front node; if the first ratio is greater than or equal to a first ratio threshold, determining to update the global pose information of the front node and the target node corresponding to the front node.

[0073] The mobile device corresponding to the preceding node is the mobile device that generates lateral deviation data at that preceding node.

[0074] The ratio between the statistical count corresponding to the preceding node and the total number of mobile devices corresponding to that preceding node is calculated to obtain a first ratio. This first ratio is then compared with a first ratio threshold. If the first ratio is greater than or equal to the first ratio threshold, it indicates that most mobile devices generate lateral deviation data at that preceding node. Therefore, this preceding node is identified as having lateral deviation, and its global pose information needs to be updated in the topology map. By analyzing the relationship between the first ratio and the first ratio threshold, the accuracy of the preceding node can be evaluated, thus identifying preceding nodes with lower accuracy as those requiring updates to their global pose information.

[0075] Based on the above technical solution, the method further includes: for each mobile device, counting the number of front nodes where the lateral deviation data is greater than the second deviation threshold; if the second ratio between the counted number of front nodes and the total number of front nodes is greater than the second ratio threshold, generating maintenance prompt information for the mobile device.

[0076] For each mobile device, the lateral deviation data of each preceding node traversed by the mobile device is compared with a second deviation threshold. The number of preceding nodes whose lateral deviation data exceeds the second deviation threshold is counted. A second ratio is determined between the counted number of preceding nodes and the total number of preceding nodes traversed by the mobile device. If the second ratio is greater than the second ratio threshold, it indicates that the mobile device itself has an error and needs inspection and repair. A repair prompt message for the mobile device is generated and reported to the operator to remind them to inspect the mobile device when time permits to prevent errors. The second ratio threshold can be set according to requirements. To ensure the accuracy of the judgment results, a higher second ratio threshold can be set, for example, 90%.

[0077] Figure 3 This is a flowchart illustrating another map updating method provided in this application embodiment. Based on the above embodiment, this application embodiment can also update the location map, such as... Figure 3 As shown, the method may include:

[0078] Step 301: Obtain the lateral deviation of each mobile device at each preceding node in the topology map. The preceding node is a node in the topology map that is adjacent to the target node and located before the target node.

[0079] Step 302: Based on the statistical information of the lateral deviation data corresponding to the same front node for each mobile device, determine whether to update the global pose information of the front node and the target node corresponding to the front node.

[0080] Step 303: If it is determined that the global pose information of the front node and the target node needs to be updated, the target global pose information of the front node and the target global pose information of the target node are determined based on the pose information of each mobile device at the front node.

[0081] Step 304: In the topology map, update the global pose information of the preceding node to the target global pose information, and update the global pose information of the target node to the target global pose information.

[0082] Step 305: Obtain the location area to be updated in the location map.

[0083] If it is known that the environment of a certain area has changed, that area can be directly identified as the location area to be updated. Alternatively, the location area to be updated can be determined based on identified preceding points; that is, if multiple preceding points are located in the same area, it can be assumed that the environment of that area has changed, and that area can be identified as the location area to be updated. The location map includes either a laser SLAM map or a visual SLAM map. The location map is a global map, while the topology map is a map of the trajectory of the mobile device within the global map.

[0084] In one embodiment of this application, obtaining the location area to be updated in the location map includes: when there are multiple preceding nodes that need to update global pose information, clustering the multiple preceding nodes to obtain at least one clustering region; for each clustering region, determining the number of mobile devices whose variance data of the lateral deviation data of each preceding node in the clustering region is greater than or equal to a variance threshold, as a statistical result; if the third ratio between the statistical result corresponding to each preceding node and the total number of the multiple mobile devices is greater than or equal to a third ratio threshold, determining the clustering region as the location area to be updated in the location map.

[0085] If multiple preceding nodes require updating global pose information, a KD-tree can be used to cluster these nodes, resulting in at least one clustered region. For each clustered region, at each preceding node, the number of mobile devices whose lateral deviation data variance is greater than or equal to a variance threshold is determined. As a statistical result, if the third ratio between the statistical result corresponding to each preceding node and the total number of mobile devices passing through that preceding node is greater than or equal to a third ratio threshold, then the clustered region can be determined as the positioning area to be updated in the positioning map. The third ratio threshold can be set according to requirements. To ensure the accuracy of the judgment, a higher third ratio threshold can be set, for example, to 90%. A KD-tree (K-dimension tree) is a tree-like data structure for storing instance points in K-dimensional space for fast retrieval. A KD-tree is a binary tree representing a partition of K-dimensional space. Constructing a KD-tree is equivalent to continuously dividing the K-dimensional space using hyperplanes perpendicular to the coordinate axes, forming a series of K-dimensional hyperrectangular regions. Each node in a KD-tree corresponds to a K-dimensional hyperrectangular region. Using KD-trees can eliminate the need to search for most data points, thus reducing the computational cost of the search.

[0086] It should be noted that, to ensure the identified location area is indeed the area that needs updating, the proportion of preceding nodes in the location area that require global pose information updates can be determined. If the proportion of preceding nodes requiring updates in the location area is greater than or equal to the fourth ratio threshold, then the location area is determined to be the location area to be updated in the location map. For example, if almost all preceding nodes in a cluster have generated lateral deviations (e.g., 90% of preceding nodes require global pose information updates), and the variance of the lateral deviation data of each mobile device at each preceding node requiring global pose information updates in this cluster is greater than (or 90%) the variance threshold, then the location map of this cluster may need updating, and this cluster is determined to be the location area to be updated in the location map.

[0087] The above method is used to determine the location area to be updated in the location map. The threshold is set relatively high. The significance of this judgment is that it is more likely that the change error of the preceding node is larger, and the probability that the location map needs to be updated is lower. Therefore, if the problem of lateral deviation can be solved by updating the topology map, there is no need to update the location map.

[0088] Step 306: Determine the target mobile device based on the variance data of the lateral deviation data of each mobile device. The frame pose and frame data of the target mobile device are used as the basis for updating the positioning map.

[0089] Typically, a location map built by a single mobile device is smoother than a location map built and stitched together by multiple mobile devices. Therefore, in this application embodiment, the variance data of the lateral deviation data of the mobile device is used to determine the mobile device with higher installation accuracy, and the mobile device is identified as the target mobile device. The target mobile device is then used to build the map to reduce the error of the constructed map.

[0090] In one embodiment of this application, determining the target mobile device based on the variance data of the lateral deviation data of each of the mobile devices includes: determining the mobile device with the smallest variance data of the lateral deviation data among the various mobile devices as the target mobile device.

[0091] By comparing the variance data of the lateral deviation data of each mobile device, the mobile device with the smallest variance data is selected and identified as the target mobile device. This method results in a more accurate target mobile device and a smaller error in the constructed positioning map.

[0092] Step 307: Obtain the frame pose and frame data of the target mobile device in the positioning area for multiple frames, and obtain the frame pose and frame data of the target mobile device in a number of frames before and adjacent to the multiple frames, as well as the frame pose and frame data of the target mobile device in a number of frames after and adjacent to the multiple frames.

[0093] Localization maps can be divided into grid maps and topological maps. For the sake of consistency, the map is updated using frame pose and frame data. The core issue is how to ensure the accuracy of the frame pose of the updated area. This application uses graph optimization to achieve this.

[0094] The frame poses of the target mobile device within the positioning area are the frame poses that need to be optimized. Select the target number of frames at the boundary of the positioning area as the welding area with the original map, obtain the frame poses and frame data of the target mobile device within the positioning area, and obtain the frame poses and frame data of the target number of frames before and adjacent to the target mobile device, as well as the frame poses and frame data of the target number of frames after and adjacent to the target mobile device.

[0095] Step 308: Optimize the frame pose of the multiple frames based on the frame pose and frame data of the multiple frames, the frame pose and frame data of the target number of frames before the multiple frames, and the frame pose and frame data of the target number of frames after the multiple frames, to obtain the optimized frame pose of the multiple frames.

[0096] A factor graph is constructed based on the frame poses and frame data of multiple frames, the frame poses and frame data of the target number of frames before the multiple frames, and the frame poses and frame data of the target number of frames after the multiple frames. The frame pose is then optimized based on the factor graph. Nonlinear least squares is used in the optimization process to determine the optimized frame poses of multiple frames.

[0097] Figure 4a This is an example image of the location area to be updated in the location map in this application embodiment. Figure 4b Yes Figure 4a An example diagram of the factor map for frame pose optimization of the localization region is shown below. Figure 4a As shown, area 6 is the positioning area to be updated, and area 7 is the welding area, as follows. Figure 4b As shown, pentagon 8 represents the frame pose of the target mobile device in the welding area, pentagon 9 represents the frame pose of the target mobile device in the positioning area, horizontal line 10 represents the feature observation constraints of the map, horizontal line 11 represents the inter-frame constraints of multiple frames in the positioning area, line 12 connecting the poses of each frame represents the odometry inter-frame constraints, and triangle 13 represents the map information. For visual SLAM and laser SLAM, the map information is represented using different data. For example, for visual SLAM, the positioning map adopts a feature-based format, and the map information can be represented using visual features extracted from the frame data. For laser SLAM, if the positioning map format has features, the map information can be represented using laser features extracted from the frame data.

[0098] When optimizing frame pose based on factor graphs, for 2D lasers in laser SLAM, Correlative Scan Matching (CSM) and gradient-based optimization methods (CREES) can be used. CSM can be simply understood as a brute-force search, matching each frame (laser) data with the pose of every frame in the subgraph (localization region) until the optimal pose is found. Gradient-based optimization methods construct an error function and minimize it, resulting in higher accuracy. For 3D lasers in laser SLAM, ICP (Iterative Closest Point) can be used for frame pose optimization based on factor graphs.

[0099] When optimizing frame pose based on factor maps, visual reprojection error can be used for optimization in visual SLAM. Visual reprojection error is the difference between the estimated value and the observed value of a feature point in the normalized camera coordinate system.

[0100] Step 309: Project the frame data of the multiple frames and the optimized frame pose onto the positioning map to obtain the updated positioning map.

[0101] After optimizing the frame poses of multiple frames, the optimized frame poses of each frame and the original frame data are projected onto the localization map to complete the localization map update, resulting in the updated localization map. Once the updated localization map is obtained, it can prompt the operator to check it and receive manual confirmation or rejection instructions from the operator. Upon receiving a confirmation instruction, the updated localization map is applied.

[0102] After completing the map update, the recorded lateral deviation data is cleared. This involves clearing the number of times, mean, and variance of lateral deviations generated by the mobile device at each preceding node, and also clearing the number of times, mean, and variance of lateral deviations generated by each mobile device at the same preceding node. In other words, the map is cleared. <robot_id,shard_ptr1 <msgs>> and map <node_id,shard_ptr2 <msgs>>

[0103] The map update method provided in this embodiment can update the positioning area that needs to be updated in the positioning map after the topology map is updated. Based on the variance data of the lateral deviation data of each mobile device, the mobile device with higher accuracy is determined as the target mobile device. Based on the frame pose and frame data of the target mobile device in the positioning area for multiple frames, the frame pose and frame data of the target number of frames before and adjacent to the multiple frames, and the frame pose and frame data of the target number of frames after and adjacent to the multiple frames, the frame pose of the multiple frames is optimized to obtain the optimized frame pose. The frame data of the multiple frames and the optimized frame pose are projected onto the positioning map to obtain the updated positioning map, realizing the automatic update of the positioning map based on the trajectory of the target mobile device.

[0104] This application employs a server-based detection and map update scheme. First, it determines whether the preceding node has lateral deviation data and calculates its mean and variance. This is determined by checking if the lateral deviations reported multiple times by multiple mobile devices at the preceding node (or the lateral deviations calculated by the server) exceed a deviation threshold, and calculating their mean and covariance. Second, it determines whether the topology map and positioning map need updating, and whether the mobile device itself has deviations. Based on the presence of lateral deviation data, it determines whether the preceding node needs updating. Based on the variance of the lateral deviation data and the number of nearby preceding nodes requiring global pose updates, it determines whether the map for that area needs reconstruction. The topology map and positioning map are then updated. Since the relative relationships between the target node and the reference point, and between the preceding node and the target node, are fixed, the poses of the preceding and target nodes can be updated based on their current poses and their relationship with the reference point. For the positioning map, a trajectory-based update method is used. Finally, the cache is cleared, and monitoring continues. After the map update is complete, the cache information of the updated node (the updated preceding node) is cleared, and monitoring restarts. This application's embodiments can solve both the problem of large point marking errors and the problem of lateral deviation of preceding nodes caused by environmental changes. It can also reasonably evaluate high-precision point marking (i.e., it can evaluate and update the lateral deviation data of preceding nodes in real time), reducing subsequent maintenance costs and improving the efficiency of mobile device tasks. With the solution provided in this application's embodiments, implementers can even avoid on-site point marking, directly marking initial points on the map, and then adjusting all points according to the solution provided in this application's embodiments, reducing implementation difficulty and improving implementation efficiency. The server-based multi-vehicle statistical solution can eliminate the differences between individual vehicles.

[0105] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of this application.

[0106] Figure 5 This is a structural block diagram of a map updating device provided in an embodiment of this application, such as... Figure 5 As shown, the map updating device may include:

[0107] The lateral deviation acquisition module 501 is used to acquire the lateral deviation data of each mobile device at each preceding node in the topology map. The preceding node is a node in the topology map that is adjacent to the target node and located before the target node. The topology map represents the running path of the mobile device. The lateral deviation data is the lateral deviation between the pose information of the mobile device at the preceding node and the reference pose information.

[0108] The update determination module 502 is used to determine whether to update the global pose information of the front node and the target node corresponding to the front node based on the statistical information of the lateral deviation data corresponding to the same front node of each mobile device.

[0109] The pose information determination module 503 is used to determine the target global pose information of the front node and the target node if it is determined that the global pose information of the front node and the target node should be updated. Based on the pose information of each mobile device at the front node, the module determines the target global pose information of the front node and the target global pose information of the target node. The global pose information of the front node represents the global reference pose information of the mobile device at the front node, and the global pose information of the target node represents the global reference pose information of the mobile device at the target node.

[0110] The topology map update module 504 is used to update the global pose information of the preceding node to the target global pose information in the topology map, and to update the global pose information of the target node to the target global pose information.

[0111] Optionally, the device further includes:

[0112] For each of the preceding nodes, the statistical number of mobile devices whose lateral deviation data is greater than a first deviation threshold at the preceding node is determined as the statistical information corresponding to the preceding node.

[0113] Optionally, the update determination module includes:

[0114] The first ratio determination unit is used to determine a first ratio between the statistical quantity and the total number of mobile devices corresponding to the front node;

[0115] The update determination unit is used to determine to update the global pose information of the preceding node and the target node corresponding to the preceding node if the first ratio is greater than or equal to the first ratio threshold.

[0116] Optionally, the pose information determination module includes:

[0117] The pose information determination unit is used to determine the global pose information of the target node and the global pose information of the front node for each mobile device, based on the pose information of the target node relative to the reference point, the pose information of the front node relative to the target node, the global pose information of the mobile device, and the pose information of the reference point relative to the mobile device. The reference point is the origin of the coordinate system for determining the pose information of the mobile device.

[0118] The mean calculation unit is used to calculate the mean of the global pose information of the target node determined under each of the mobile devices to obtain the target global pose information of the target node, and to calculate the mean of the global pose information of the front node determined under each of the mobile devices to obtain the target global pose information of the front node.

[0119] Optionally, the mean calculation unit is specifically used for:

[0120] Remove the maximum and minimum global pose information from the global pose information of the target node determined under each of the mobile devices, and determine the average of the remaining global pose information as the target global pose information of the target node.

[0121] Remove the maximum and minimum global pose information from the global pose information of the front node determined under each of the mobile devices, and determine the average of the remaining global pose information as the target global pose information of the front node.

[0122] Optionally, the device further includes:

[0123] The second statistics module is used to count the number of preceding nodes for each mobile device where the lateral deviation data is greater than the second deviation threshold.

[0124] The prompt message generation module is used to generate maintenance prompt messages for the mobile device if the second ratio between the number of front nodes obtained by statistics and the total number of front nodes is greater than the second ratio threshold.

[0125] Optionally, the device further includes:

[0126] The location area acquisition module is used to acquire the location area to be updated in the location map;

[0127] The target device determination module is used to determine the target mobile device based on the variance data of the lateral deviation data of each mobile device, wherein the frame pose and frame data of the target mobile device are used as the basis for updating the positioning map.

[0128] The pose data acquisition module is used to acquire the frame pose and frame data of the target mobile device in the positioning area for multiple frames, and to acquire the frame pose and frame data of the target mobile device in the target number of frames before and adjacent to the multiple frames, as well as the frame pose and frame data of the target mobile device in the target number of frames after and adjacent to the multiple frames.

[0129] The frame pose optimization module is used to optimize the frame pose of the multiple frames based on the frame pose and frame data of the multiple frames, the frame pose and frame data of a target number of frames before the multiple frames, and the frame pose and frame data of a target number of frames after the multiple frames, so as to obtain the optimized frame pose of the multiple frames.

[0130] The positioning map update module is used to project the frame data of the multiple frames and the optimized frame pose onto the positioning map to obtain the updated positioning map.

[0131] Optionally, the positioning area acquisition module includes:

[0132] A region clustering unit is used to cluster multiple preceding nodes that need to update global pose information when there are multiple preceding nodes, to obtain at least one clustering region.

[0133] The third statistical unit is used to determine, for each cluster region, the number of mobile devices whose variance data of the lateral deviation data at each preceding node in the cluster region is greater than or equal to the variance threshold, as a statistical result.

[0134] The location area determination unit is used to determine the clustered area as the location area to be updated in the location map if the third ratio between the statistical result corresponding to each of the preceding nodes and the total number of the plurality of mobile devices is greater than or equal to the third ratio threshold.

[0135] Optionally, the lateral deviation acquisition module includes:

[0136] A lateral deviation receiving unit is configured to receive lateral deviation data reported by each of the mobile devices at each of the preceding nodes; or

[0137] The lateral deviation determination unit is used to receive the pose information of the mobile device relative to the reference point reported by each of the mobile devices, and to determine the lateral deviation data of the mobile device at the preceding node corresponding to the reference point based on the pose information. The reference point is the origin of the coordinate system for determining the pose information.

[0138] For the specific implementation process of the functions corresponding to each module and unit in the device provided in this application embodiment, please refer to... Figure 1 -The method embodiment shown in Figure 4 will not be described in detail here regarding the specific implementation process of the functions corresponding to each module and unit of the device.

[0139] The map updating device provided in this embodiment acquires the lateral deviation data of each mobile device at each preceding node in the topology map. Based on the statistical information of the lateral deviation data corresponding to the same preceding node for each mobile device, it determines whether to update the global pose information of the preceding node and the target node corresponding to the preceding node. If it is determined that the global pose information of the preceding node and the target node should be updated, the target global pose information of the preceding node and the target global pose information of the target node are determined based on the pose information of each mobile device at the preceding node. In the topology map, the global pose information of the preceding node is updated to the target global pose information of the preceding node, and the global pose information of the target node is updated to the target global pose information of the target node. Since the preceding node and target node whose global pose information needs to be updated can be determined based on the lateral deviation data of the mobile device passing through the preceding node, and then the global pose information of the preceding node and the target node is updated, the topology map is automatically updated without re-marking points, which can reduce maintenance costs and improve the efficiency of mobile device tasks.

[0140] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0141] Figure 6 This is a structural block diagram of an electronic device provided in an embodiment of this application, such as... Figure 6 As shown, the electronic device 600 may include one or more processors 610 and one or more memories 620 connected to the processors 610. The electronic device 600 may also include an input interface 630 and an output interface 640 for communicating with another device or system. Program code executed by the processor 610 may be stored in the memory 620.

[0142] The processor 610 in the electronic device 600 calls the program code stored in the memory 620 to execute the map update method in the above embodiment.

[0143] According to one embodiment of this application, a computer-readable storage medium is also provided, including but not limited to disk storage, CD-ROM, optical storage, etc., wherein a computer program is stored on the computer-readable storage medium, and the computer program implements the map update method described in the foregoing embodiment when executed by a processor.

[0144] According to one embodiment of this application, a computer program product is also provided, including a computer program or computer instructions, which, when executed by a processor, implement the map update method described in the above embodiments.

[0145] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0146] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented 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.

[0147] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should 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 terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0148] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate 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.

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal 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.

[0150] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0151] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device 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 terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0152] The above provides a detailed description of the map updating method, electronic device, and storage medium provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.< / msgs> < / msgs> < / msgs> < / msgs> < / msgs>

Claims

1. A map updating method, characterized in that, include: The lateral deviation data of each mobile device at each preceding node in the topology map is obtained. The preceding node is a node that is adjacent to the target node and located before the target node in the topology map. The topology map represents the running path of the mobile device. The lateral deviation data is the lateral deviation between the pose information of the mobile device at the preceding node and the reference pose information. Based on the statistical information of the lateral deviation data corresponding to the same front node for each mobile device, determine whether to update the global pose information of the front node and the target node corresponding to the front node. For each of the preceding nodes, the number of mobile devices whose lateral deviation data is greater than a first deviation threshold at the preceding node is determined as the statistical information corresponding to the preceding node; If it is determined that the global pose information of the front node and the target node should be updated, the target global pose information of the front node and the target global pose information of the target node are determined according to the pose information of each mobile device at the front node. The global pose information of the front node represents the global reference pose information of the mobile device at the front node, and the global pose information of the target node represents the global reference pose information of the mobile device at the target node. In the topology map, the global pose information of the preceding node is updated to the target global pose information, and the global pose information of the target node is updated to the target global pose information. The step of determining the target global pose information of the front-end node and the target global pose information of the target node based on the pose information of each mobile device at the front-end node includes: For each of the mobile devices, the global pose information of the target node and the global pose information of the front node are determined based on the pose information of the target node relative to the reference point, the pose information of the front node relative to the target node, the global pose information of the mobile device, and the pose information of the reference point relative to the mobile device. The reference point is the origin of the coordinate system for determining the pose information of the mobile device. The target global pose information of the target node is obtained by averaging the global pose information of the target node determined under each of the mobile devices, and the target global pose information of the front node is obtained by averaging the global pose information of the front node determined under each of the mobile devices.

2. The method according to claim 1, characterized in that, The step of determining whether to update the global pose information of the front node and the target node corresponding to the front node based on the statistical information of the lateral deviation data corresponding to each mobile device at the same front node includes: Determine a first ratio between the statistical count and the total number of mobile devices corresponding to the front node; If the first ratio is greater than or equal to the first ratio threshold, it is determined that the global pose information of the preceding node and the target node corresponding to the preceding node should be updated.

3. The method according to claim 1, characterized in that, The step of averaging the global pose information of the target nodes determined under each of the mobile devices to obtain the target global pose information of the target nodes includes: Remove the maximum and minimum global pose information from the global pose information of the target node determined under each of the mobile devices, and determine the average of the remaining global pose information as the target global pose information of the target node. The step of averaging the global pose information of the determined front nodes under each of the mobile devices to obtain the target global pose information of the front nodes includes: Remove the maximum and minimum global pose information from the global pose information of the front node determined under each of the mobile devices, and determine the average of the remaining global pose information as the target global pose information of the front node.

4. The method according to any one of claims 1-2, characterized in that, Also includes: For each of the mobile devices, count the number of preceding nodes where the lateral deviation data is greater than the second deviation threshold; If the second ratio between the number of front-end nodes obtained from the statistics and the total number of front-end nodes is greater than the second ratio threshold, maintenance prompt information for the mobile device is generated.

5. The method according to any one of claims 1-2, characterized in that, Also includes: Get the location area to be updated in the location map; The target mobile device is determined based on the variance data of the lateral deviation data of each mobile device, and the frame pose and frame data of the target mobile device are used as the basis for updating the positioning map. The frame pose and frame data of the target mobile device in the positioning area for multiple frames are obtained, and the frame pose and frame data of the target mobile device in the target number of frames before and adjacent to the multiple frames are obtained, as well as the frame pose and frame data of the target mobile device in the target number of frames after and adjacent to the multiple frames are obtained. Based on the frame poses and frame data of the multiple frames, the frame poses and frame data of a target number of frames before the multiple frames, and the frame poses and frame data of a target number of frames after the multiple frames, the frame poses of the multiple frames are optimized to obtain the optimized frame poses of the multiple frames. The frame data of the multiple frames and the optimized frame pose are projected onto the localization map to obtain the updated localization map.

6. The method according to claim 5, characterized in that, The step of obtaining the location area to be updated in the location map includes: When there are multiple preceding nodes that need to update global pose information, the multiple preceding nodes are clustered to obtain at least one clustering region. For each cluster region, the number of mobile devices whose variance data of the lateral deviation data at each preceding node in the cluster region is greater than or equal to the variance threshold is determined as a statistical result. If the third ratio between the statistical result corresponding to each of the preceding nodes and the total number of the multiple mobile devices is greater than or equal to the third ratio threshold, the clustered region is determined to be the location region to be updated in the location map.

7. The method according to any one of claims 1-2, characterized in that, The step of obtaining the lateral deviation data of each mobile device at each preceding node in the topology map includes: Receive lateral deviation data reported by each of the aforementioned mobile devices at each of the aforementioned front-end nodes; or The system receives pose information of each mobile device relative to a reference point, and determines the lateral deviation data of the mobile device at the preceding node corresponding to the reference point based on the pose information. The reference point is the origin of the coordinate system used to determine the pose information.

8. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the map update method as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the map update method as described in any one of claims 1-7.

10. A computer program product, characterized in that, It includes a computer program or computer instructions that, when executed by a processor, implement the map update method according to any one of claims 1 to 7.

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