High-precision map environment change detection method, device and equipment and automatic driving vehicle

Abstract

The present disclosure provides an environment change detection method and device, electronic equipment and storage medium, relates to the technical field of automatic driving and intelligent transportation, in particular to the technical field of high-precision map and big data processing, and can be applied to the scene of map updating. The scheme comprises: dividing online point cloud data into a plurality of online data grids; determining a map unit corresponding to each online data grid from an offline map; for each online data grid, calculating a height difference between a point cloud height value of the online data grid and a point cloud height value of the corresponding map unit, and generating a change point corresponding to the online data unit when it is determined that the height difference is greater than a height difference threshold; calculating distribution feature information for all change points, and determining that the region corresponding to the online point cloud data has undergone an environment change in response to the distribution feature information meeting a distribution feature condition. The above method facilitates the use of less computing resources to complete the detection process, and helps to improve the efficiency and accuracy of the environment change detection.

Description

Technical Field

[0001] This disclosure relates to the fields of autonomous driving and intelligent transportation technologies, specifically high-precision maps and big data processing technologies, and can be applied to scenarios such as map updates. Background Technology

[0002] High-precision maps, also known as high-resolution maps, are used by autonomous vehicles. They possess precise vehicle location information and rich road element data, helping cars anticipate complex road conditions such as slope, curvature, and heading, thus better avoiding potential risks. Compared to ordinary maps, high-precision maps have a larger data scale, requiring higher processing performance and efficiency. The generation of high-precision maps involves creating 3D maps based on detected point cloud data from the real environment. When the environment changes, the data in the 3D map may not reflect the actual reality, necessitating regular environmental change detection to update the data in the 3D map promptly.

[0003] In related technologies, changes in the environment can be detected by comparing the latest point cloud data with data from a 3D map. However, some existing methods for detecting environmental changes cannot simultaneously achieve both detection efficiency and accuracy. Summary of the Invention

[0004] This disclosure provides an environmental change detection method, apparatus, electronic device, and storage medium.

[0005] According to a first aspect of this disclosure, an environmental change detection method is provided, the environmental change detection method comprising:

[0006] The online point cloud data is divided into multiple online data rasters;

[0007] Identify the map unit corresponding to each online data raster from the offline map;

[0008] For each online data raster, calculate the height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding map unit. When the height difference is greater than the height difference threshold, generate the change point corresponding to the online data unit.

[0009] Calculate the distribution characteristic information for all change points. In response to the distribution characteristic information meeting the distribution characteristic conditions, determine that the area corresponding to the online point cloud data has undergone environmental changes.

[0010] In this embodiment of the disclosure, the offline map includes multiple map data layers, each map data layer contains point cloud data of a corresponding height range in the offline map, and each map data layer has been divided into multiple offline point cloud grids; determining the map unit corresponding to each online data grid from the offline map includes:

[0011] Based on the maximum height range of point clouds in online point cloud data, at least one target map data layer is determined from the offline map, wherein the maximum height range is within the total height range of at least one target map data layer;

[0012] Merge offline point cloud rasters with the same horizontal coordinates in at least one target map data layer into a single map unit;

[0013] Based on the horizontal coordinates of each online data raster, the map unit corresponding to each online data raster is determined.

[0014] In this embodiment of the disclosure, each map unit corresponding to an online data raster includes a first map unit and at least one second map unit;

[0015] The horizontal coordinates of the first map cell are the same as the horizontal coordinates of the online data raster, and the second map cell is within a preset distance range of the first map cell.

[0016] In this embodiment of the disclosure, for each online data raster, the height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding map unit is calculated. When the height difference is determined to be greater than a height difference threshold, a change point corresponding to the online data unit is generated, including:

[0017] For each online data raster, calculate the first height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding first map unit, and the second height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding second map unit.

[0018] When the first height difference and each second height difference are both greater than the height difference threshold, change points corresponding to the online data units are generated.

[0019] In this embodiment of the disclosure, the coordinates of the change point corresponding to the online data unit are generated based on the horizontal coordinates of the online data raster.

[0020] In this embodiment of the disclosure, distribution feature information for all change points is calculated. In response to the distribution feature information meeting the distribution feature conditions, it is determined that the region corresponding to the online point cloud data has undergone environmental change, including:

[0021] Cluster all change points to obtain at least one change point cluster;

[0022] Calculate the distribution characteristics of each cluster of change points;

[0023] In response to determining that the distribution characteristic information of at least one changed point cluster meets the distribution characteristic conditions, it is determined that the region corresponding to the point cloud data associated with the changed point cluster in the online point cloud data has undergone environmental change.

[0024] In this embodiment of the disclosure, the distribution characteristic information of the change point cluster includes: the distance between the centroid of the change point cluster and the environmental data acquisition device;

[0025] The distribution characteristics include: the distance between the centroid of the change point cluster and the environmental data acquisition device is less than a preset distance threshold;

[0026] An environmental data acquisition device is a device used to acquire online point cloud data;

[0027] The distance between the centroid of the change point cluster and the environmental data acquisition device is calculated based on the location information of the centroid of the change point cluster and the location information of the environmental data acquisition device.

[0028] The location information of the centroid of the change point cluster is calculated based on the location information of the change points in the change point cluster.

[0029] In this embodiment of the disclosure, the position information of the centroid of the change point cluster includes the first axial coordinate and the second axial coordinate;

[0030] The first axial coordinate of the centroid of the change point cluster is the average of the first axial coordinates of all change points in the change point cluster.

[0031] The second axial coordinate of the centroid of the change point cluster is the average of the second axial coordinates of all change points in the change point cluster.

[0032] In this embodiment of the disclosure, the distribution feature information of the change point cluster further includes: the length of the main distribution direction axis of the change points in the change point cluster;

[0033] The distribution characteristic conditions include: the length of the main distribution direction axis of the change points in the change point cluster is greater than the preset length threshold.

[0034] In this embodiment of the disclosure, the length of the main distribution direction axis is calculated based on the position information of the centroid of the change point cluster and the position information of each change point in the change point cluster.

[0035] In this embodiment of the disclosure, the distribution characteristic information of the change point cluster further includes: the distribution density of change points in the change point cluster;

[0036] The distribution characteristic conditions include: the distribution density of change points in the change point cluster is greater than the preset density threshold.

[0037] In this embodiment of the disclosure, the distribution density of change points in the change point cluster is calculated based on the number of change points in the change point cluster and the distribution area of ​​the change point cluster;

[0038] The distribution area of ​​the change point cluster is the area of ​​an ellipse with the main distribution direction axis of the change points in the change point cluster as the major axis and the secondary distribution direction axis of the change points in the change point cluster as the minor axis.

[0039] The lengths of the main distribution direction axis and the secondary distribution direction axis are calculated based on the position information of the centroid of the change point cluster and the position information of each change point in the change point cluster.

[0040] According to a second aspect of this disclosure, an environmental change detection device is provided, which includes an online point cloud processing module, a map unit determination module, a change point generation module, and a change detection module.

[0041] The online point cloud processing module is used to divide online point cloud data into multiple online data rasters;

[0042] The map cell determination module is used to determine the map cell corresponding to each online data raster from the offline map;

[0043] The change point generation module is used to calculate the height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding map unit for each online data raster. When the height difference is determined to be greater than the height difference threshold, the change point corresponding to the online data unit is generated.

[0044] The change detection module is used to calculate the distribution feature information for all change points. In response to the distribution feature information meeting the distribution feature conditions, it determines that the area corresponding to the online point cloud data has undergone environmental changes.

[0045] In this embodiment of the disclosure, the offline map includes multiple map data layers, each map data layer contains point cloud data of a corresponding height range in the offline map, and each map data layer has been divided into multiple offline point cloud grids;

[0046] The map cell determination module, when used to determine the map cell corresponding to each online data raster from the offline map, is specifically used for:

[0047] Based on the maximum height range of point clouds in online point cloud data, at least one target map data layer is determined from the offline map, wherein the maximum height range is within the total height range of at least one target map data layer;

[0048] Merge offline point cloud rasters with the same horizontal coordinates in at least one target map data layer into a single map unit;

[0049] Based on the horizontal coordinates of each online data raster, the map unit corresponding to each online data raster is determined.

[0050] In this embodiment of the disclosure, each map unit corresponding to an online data raster includes a first map unit and at least one second map unit;

[0051] The horizontal coordinates of the first map cell are the same as the horizontal coordinates of the online data raster, and the second map cell is within a preset distance range of the first map cell.

[0052] In this embodiment of the disclosure, the change point generation module, when calculating the height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding map unit for each online data raster, and generating a change point corresponding to the online data unit when the height difference is determined to be greater than a height difference threshold, is specifically used for:

[0053] For each online data raster, calculate the first height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding first map unit, and the second height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding second map unit.

[0054] When the first height difference and each second height difference are both greater than the height difference threshold, change points corresponding to the online data units are generated.

[0055] In this embodiment of the disclosure, the coordinates of the change point corresponding to the online data unit are generated based on the horizontal coordinates of the online data raster.

[0056] In this embodiment of the disclosure, the change detection module, when calculating the distribution feature information for all change points, and determining that the area corresponding to the online point cloud data has undergone environmental change in response to the distribution feature information meeting the distribution feature conditions, is specifically used for:

[0057] Cluster all change points to obtain at least one change point cluster;

[0058] Calculate the distribution characteristics of each cluster of change points;

[0059] In response to determining that the distribution characteristic information of at least one changed point cluster meets the distribution characteristic conditions, it is determined that the region corresponding to the point cloud data associated with the changed point cluster in the online point cloud data has undergone environmental change.

[0060] In this embodiment of the disclosure, the distribution characteristic information of the change point cluster includes: the distance between the centroid of the change point cluster and the environmental data acquisition device;

[0061] The distribution characteristics include: the distance between the centroid of the change point cluster and the environmental data acquisition device is less than a preset distance threshold;

[0062] An environmental data acquisition device is a device used to acquire online point cloud data;

[0063] The distance between the centroid of the change point cluster and the environmental data acquisition device is calculated based on the location information of the centroid of the change point cluster and the location information of the environmental data acquisition device.

[0064] The location information of the centroid of the change point cluster is calculated based on the location information of the change points in the change point cluster.

[0065] In this embodiment of the disclosure, the position information of the centroid of the change point cluster includes the first axial coordinate and the second axial coordinate;

[0066] The first axial coordinate of the centroid of the change point cluster is the average of the first axial coordinates of all change points in the change point cluster.

[0067] The second axial coordinate of the centroid of the change point cluster is the average of the second axial coordinates of all change points in the change point cluster.

[0068] In this embodiment of the disclosure, the distribution feature information of the change point cluster further includes: the length of the main distribution direction axis of the change points in the change point cluster;

[0069] The distribution characteristic conditions include: the length of the main distribution direction axis of the change points in the change point cluster is greater than the preset length threshold.

[0070] In this embodiment of the disclosure, the length of the main distribution direction axis is calculated based on the position information of the centroid of the change point cluster and the position information of each change point in the change point cluster.

[0071] In this embodiment of the disclosure, the distribution characteristic information of the change point cluster further includes: the distribution density of change points in the change point cluster;

[0072] The distribution characteristic conditions include: the distribution density of change points in the change point cluster is greater than the preset density threshold.

[0073] In this embodiment of the disclosure, the distribution density of change points in the change point cluster is calculated based on the number of change points in the change point cluster and the distribution area of ​​the change point cluster;

[0074] The distribution area of ​​the change point cluster is the area of ​​an ellipse with the main distribution direction axis of the change points in the change point cluster as the major axis and the secondary distribution direction axis of the change points in the change point cluster as the minor axis.

[0075] The lengths of the main distribution direction axis and the secondary distribution direction axis are calculated based on the position information of the centroid of the change point cluster and the position information of each change point in the change point cluster.

[0076] According to a third aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described in the first aspect.

[0077] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions for causing a computer to perform the method described in the first aspect.

[0078] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method according to the first aspect.

[0079] According to a sixth aspect of this disclosure, an autonomous vehicle is provided, which includes the electronic equipment provided in a third aspect of this disclosure.

[0080] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description.

[0081] The beneficial effects of the technical solution provided in this disclosure are:

[0082] The environmental change detection method provided in this disclosure can rasterize offline maps and online point cloud data, compare differences on a grid-by-grid basis, initially screen out grids where environmental changes have occurred, and then determine the final environmental change result by analyzing the distribution of these grids. This process allows for the use of fewer computing resources to complete the detection process, eliminating the need to download online point cloud data locally, enabling the detection process to be performed online in real time, thus improving the efficiency of environmental change detection. Furthermore, the detection of online point cloud data has a high degree of precision, which helps improve the accuracy of the environmental change detection results. Attached Figure Description

[0083] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0084] Figure 1 A flowchart illustrating an environmental change detection method provided in this disclosure is shown.

[0085] Figure 2 This illustration shows a schematic diagram of the data structure of the offline map provided in this disclosure;

[0086] Figure 3A schematic flowchart illustrating the change points corresponding to the online data units generated in this disclosure is shown;

[0087] Figure 4 A schematic diagram of an environmental change detection device provided in this disclosure is shown;

[0088] Figure 5 A schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure is shown. Detailed Implementation

[0089] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0090] It should be understood that in the embodiments of this disclosure, the character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated.

[0091] 3D maps can be generated based on point cloud data detected in the real environment. When the environment changes, the data in the 3D map may not match the actual situation in the real environment. Therefore, it is necessary to periodically detect environmental changes in order to update the data in the 3D map in a timely manner.

[0092] In related technologies, changes in the environment can be detected by comparing the latest point cloud data with data from a 3D map. However, some existing methods for detecting environmental changes cannot simultaneously achieve both detection efficiency and accuracy.

[0093] The environmental change detection method provided in this disclosure can rasterize offline maps and online point cloud data, compare differences on a grid-by-grid basis, initially screen out grids where environmental changes have occurred, and then determine the final environmental change result by analyzing the distribution of these grids. This process allows for the use of fewer computing resources to complete the detection process, eliminating the need to download online point cloud data locally, enabling the detection process to be performed online in real time, thus improving the efficiency of environmental change detection. Furthermore, the detection of online point cloud data has a high degree of precision, which helps improve the accuracy of the environmental change detection results.

[0094] The execution entity of this method can be a terminal device, a computer, a server, or other devices with data processing capabilities. No limitation is placed on the execution entity of this method. In some embodiments, the environmental change detection method provided in this disclosure is executed by a terminal device (such as an onboard computer) on a vehicle.

[0095] Optionally, the terminal device can be a mobile phone, tablet computer, wearable device, in-vehicle device, augmented reality (AR) / virtual reality (VR) device, laptop computer, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), etc. The specific type of terminal device is not limited in the embodiments of this disclosure.

[0096] In some embodiments, the server can be a single server, or it can be a server cluster consisting of multiple servers. In some embodiments, the server cluster can also be a distributed cluster. This disclosure does not limit the specific implementation of the server.

[0097] The environmental change detection method provided in this disclosure is illustrated below.

[0098] This disclosure provides an environmental change detection method that determines whether an environmental change has occurred in a certain area based on online point cloud data and offline maps. The online point cloud data is generated based on the latest environmental data collected by an environmental data acquisition device for a specific area; it can be understood that the online point cloud data contains the location information of multiple measurement points in that area.

[0099] Offline maps are generated based on environmental data collected by environmental data acquisition devices over a historical period. Specifically, point cloud data can be generated from environmental data collected from multiple areas over a historical period. Then, using online real-time positioning technology or offline point cloud registration technology, a corresponding 3D model is generated from the point cloud data, and an offline map is created based on this 3D model.

[0100] Here, the environmental data acquisition device can be a LiDAR (Light Detection and Ranging) system. LiDAR features high resolution and good low-altitude detection capabilities, and is widely used in fields such as autonomous driving, urban planning, and resource exploration. Of course, other types of environmental data acquisition devices capable of acquiring point cloud data will not be listed here.

[0101] It is understood that the embodiments of this disclosure can convert the position information of measurement points in online point cloud data and the position information of measurement points in offline maps into the same three-dimensional coordinate system. The position information of the measurement points includes a first axial coordinate (e.g., x-axis coordinate), a second axial coordinate (e.g., y-axis coordinate), and a third axial coordinate (e.g., z-axis coordinate), wherein the first and second axial coordinates represent the horizontal position of the measurement point, and the third axial coordinate represents the height of the measurement point.

[0102] Figure 1 A flowchart illustrating an environmental change detection method provided in this disclosure is shown, such as... Figure 1 As shown, the method mainly includes the following steps:

[0103] S110: Divide online point cloud data into multiple online data rasters.

[0104] Online point cloud data can be obtained by compressing single or multiple frames of environmental data of a certain area recently acquired by an environmental data acquisition device. In this embodiment, the online point cloud data can be divided into several grids in the horizontal direction according to a preset resolution, thereby obtaining multiple online data grids. Here, the online data grid can be an assumed spatial region, and the online data grid contains multiple measurement points in the online point cloud data.

[0105] S120: Determine the map cell corresponding to each online data raster from the offline map.

[0106] It is understood that a map cell can be a hypothetical spatial area, containing multiple measurement points in the offline map. Embodiments of this disclosure can determine the map cell corresponding to an online data raster from the offline map based on the location information of the online data raster and the map cell itself.

[0107] In this embodiment of the disclosure, the offline map includes multiple map data layers, each map data layer contains point cloud data of a corresponding height range in the offline map, and each map data layer has been divided into multiple offline point cloud grids according to a preset resolution.

[0108] Figure 2 This diagram illustrates the data structure of the offline map provided in an embodiment of the present disclosure. Figure 2 Six map data layers corresponding to different altitude ranges are shown, specifically map data layers L1 to L6. Figure 2 In the map, rectangular grids crossed by the same dashed line are offline point cloud grids belonging to the same map data layer. For example, offline point cloud grids L11 and L18 both belong to map data layer L1.

[0109] In this embodiment of the disclosure, the height range of offline point cloud grids in the same map data layer is the same, and the height ranges of adjacent map data layers may overlap, such as... Figure 2 As shown, the maximum height of the offline point cloud raster L11 in map data layer L1 can be greater than the minimum height of the offline point cloud raster L21 in map data layer L2.

[0110] It should be noted that the resolution used when dividing the offline point cloud raster is the same as the resolution used when dividing the online data raster; and each online data raster corresponds to at least one offline point cloud raster, and the horizontal coordinates (i.e., the first axis coordinate and the second axis coordinate) of the online data raster and the corresponding offline point cloud raster are the same.

[0111] In this embodiment, when determining the map unit corresponding to each online data raster from the offline map, at least one target map data layer can be determined from the offline map based on the maximum height range of the point cloud in the online point cloud data. Then, offline point cloud rasters with the same horizontal coordinates in the at least one target map data layer are merged into one map unit. Finally, the map unit corresponding to each online data raster is determined based on the horizontal coordinates of each online data raster. By accurately determining a portion of map data related to the online point cloud data from the offline map based on the maximum height range of the point cloud in the online point cloud data, the subsequent comparison process becomes more targeted, improving detection efficiency and saving computational resources.

[0112] It is understandable that the maximum height range of point clouds in online point cloud data includes both minimum and maximum heights. When determining the target map data layer from the offline map, the map data layer whose minimum height is closest to the minimum height of the online point cloud data, and whose minimum height is less than or equal to the minimum height of the online point cloud data, can be identified from the offline map and designated as the first target map data layer. Similarly, the map data layer whose maximum height is closest to the maximum height of the online point cloud data, and whose maximum height is greater than or equal to the maximum height of the online point cloud data, can be identified from the offline map and designated as the second target map data layer. Subsequently, all other map data layers between the first and second target map data layers are also considered as target map data layers.

[0113] by Figure 2For example, for an online data raster, the minimum height of map data layer L1 is less than the minimum height of the online data raster, and the minimum height of map data layer L1 is closest to the minimum height of the online data raster. Therefore, map data layer L1 can be identified as a target map data layer. The maximum height of map data layer L3 is greater than the maximum height of the online data raster, and the maximum height of map data layer L3 is closest to the maximum height of the online data raster. Therefore, map data layer L3 can be identified as a target map data layer. Furthermore, map data layer L2, located between map data layers L1 and L3, can also be identified as a target map data layer. Offline point cloud rasters with the same horizontal coordinates in map data layers L1, L2, and L3 are merged into a single map unit. For example, offline point cloud rasters L11, L21, and L31 can be merged into a single map unit. After merging the map units, for each online data raster, map units that are associated with the horizontal coordinates of that online data raster are identified, and these identified map units are used as the map units corresponding to that online data raster.

[0114] In some embodiments, the point cloud height value of each offline point cloud raster in the offline map can be pre-calculated and stored. When merging multiple offline point cloud rasteres into a map unit, the point cloud height value of the map unit can be calculated based on the point cloud height values ​​of the individual offline point cloud rasteres. This can save time and computational resources in calculating the point cloud height value of the map unit. For example, the weighted average of the point cloud height values ​​of the multiple offline point cloud rasteres of the map unit can be used as the point cloud height value of the map unit, wherein the weight coefficient corresponding to the point cloud height value of the offline point cloud raster is proportional to the number of measurement points in the offline point cloud raster.

[0115] S130: For each online data raster, calculate the height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding map unit. When the height difference is determined to be greater than the height difference threshold, generate the change point corresponding to the online data unit.

[0116] In this embodiment of the disclosure, the point cloud height value may be the average height of all measurement points in the online data raster (or map cell), or the point cloud height value may be the weighted average height of all measurement points in the online data raster (or map cell), or the median height of all measurement points in the online data raster (or map cell), etc.

[0117] In some embodiments, each online data raster may correspond to multiple map units. Optionally, the map unit corresponding to each online data raster includes a first map unit and at least one second map unit. Here, the horizontal coordinates of the first map unit are the same as the horizontal coordinates of the online data raster, and the second map unit is within a preset distance range of the first map unit. In this case, for each online data raster, a first height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding first map unit, and a second height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding second map unit can be calculated; when it is determined that both the first height difference and each second height difference are greater than a height difference threshold, a change point corresponding to the online data unit is generated. The online data raster is compared with multiple associated map units to ensure that there is a large deviation in the comparison results, thereby ensuring the accuracy of the final environmental change detection results.

[0118] Figure 3 This invention discloses a schematic flowchart illustrating the generation of change points corresponding to online data units according to an embodiment of the present disclosure. Figure 3 The right side of the middle section is a schematic diagram showing the distribution of the generated change points. Figure 3 In this design, both online data units and map units are projected onto a horizontal plane as squares with sides of 0.015 meters. One online data unit corresponds to a 17x17 map unit matrix, where the map unit in the center of the matrix is ​​the first map unit mentioned earlier, and the remaining map units are the second map units mentioned earlier. It is understood that when the height difference between the point cloud height value of the online data raster and the point cloud height value of each map unit in the map unit matrix is ​​greater than a height difference threshold, a change point corresponding to the online data unit is generated. It is understood that the specific value of the height difference threshold can be determined according to actual design needs.

[0119] In some embodiments, the coordinates of the change point corresponding to the online data unit are generated based on the horizontal coordinates of the online data raster. For example, the coordinates of the change point corresponding to the online data unit can be the horizontal coordinates of a vertex of the online data raster; or, the coordinates of the change point corresponding to the online data unit can be the horizontal coordinates of the center point of the online data raster. Of course, the coordinates of the change point corresponding to the online data unit can also be calculated based on the horizontal coordinates of the online data raster and a preset formula.

[0120] S140: Calculate the distribution characteristic information for all change points. In response to the distribution characteristic information meeting the distribution characteristic conditions, determine that the area corresponding to the online point cloud data has undergone environmental changes.

[0121] Distribution characteristic information can reflect the distribution characteristics of change points. By setting corresponding distribution characteristic conditions for the information, if the distribution characteristic information meets these conditions, it can be determined that the area corresponding to the online point cloud data has undergone environmental changes.

[0122] The environmental change detection method provided in this disclosure can rasterize offline maps and online point cloud data, compare differences on a grid-by-grid basis, initially screen out grids where environmental changes have occurred, and then determine the final environmental change result by analyzing the distribution of these grids. This process allows for the use of fewer computing resources to complete the detection process, eliminating the need to download online point cloud data locally, enabling the detection process to be performed online in real time, thus improving the efficiency of environmental change detection. Furthermore, the detection of online point cloud data has a high degree of precision, which helps improve the accuracy of the environmental change detection results.

[0123] In some embodiments, when determining whether an area corresponding to online point cloud data has undergone environmental change, all changed points can be clustered to obtain at least one changed point cluster. Distribution characteristic information of each changed point cluster is calculated. In response to determining that the distribution characteristic information of at least one changed point cluster meets the distribution characteristic conditions, it is determined that the area corresponding to the point cloud data associated with the changed point cluster in the online point cloud data has undergone environmental change. Here, a changed point cluster includes several changed points that are close to each other. Embodiments of this disclosure can cluster the changed points based on a region growing algorithm, using Euclidean distance or Manhattan distance. By clustering the changed points, targeted analysis can be performed on different changed point clusters, thereby enabling the determination of a more refined environmental change result for a specific area.

[0124] In some embodiments, the distribution characteristic information of the change point cluster includes the distance between the centroid of the change point cluster and the environmental data acquisition device. In this case, the distribution characteristic condition includes: the distance between the centroid of the change point cluster and the environmental data acquisition device is less than a preset distance threshold. When the distance between the centroid of the change point cluster and the environmental data acquisition device is less than the preset distance threshold, it indicates that the measurement points associated with the change point cluster in the online point cloud data are within the effective detection range of the environmental data acquisition device. This ensures that the point cloud data corresponding to the change point cluster is accurate, thus ensuring the accuracy of the environmental change detection results.

[0125] Specifically, the location information of the centroid of the change point cluster is calculated based on the location information of the change points in the change point cluster. Then, the distance between the centroid of the change point cluster and the environmental data acquisition device is calculated based on the location information of the centroid of the change point cluster and the location information of the environmental data acquisition device. The distance between the centroid of the change point cluster and the environmental data acquisition device is compared with a preset distance threshold. If the distance between the centroid of the change point cluster and the environmental data acquisition device is less than the preset distance threshold, it can be determined that the area corresponding to the online point cloud data has undergone environmental change.

[0126] In this embodiment of the disclosure, the position information of the centroid of the change point cluster includes a first axial coordinate and a second axial coordinate. The first axial coordinate of the centroid of the change point cluster is the average of the first axial coordinates of all change points in the change point cluster; the second axial coordinate of the centroid of the change point cluster is the average of the second axial coordinates of all change points in the change point cluster.

[0127] Specifically, the position information of the centroid of the change point cluster can be calculated using the following formula:

[0128]

[0129] In the above formula, The first axial coordinate of the centroid of the changing point cluster is , The second axial coordinate, x, represents the centroid of the changing point cluster. i This represents the first axial coordinate, y, of a change point within a change point cluster. i Let represent the axial coordinate of a change point within a change point cluster, and n represent the number of change points in the cluster. This represents the average of the first axial coordinates of all changed points in the changed point cluster. The second axial coordinate represents the centroid of the changing point cluster. This represents the average value of the second axial coordinate of all change points in the change point cluster.

[0130] In some embodiments, based on determining whether the region corresponding to the online point cloud data has undergone environmental changes based on the distance between the centroid of the change point cluster and the environmental data acquisition device, the length of the main distribution direction axis of the change points in the change point cluster can be further introduced, and the region corresponding to the online point cloud data can be further determined based on this length.

[0131] Specifically, the distribution characteristics of the change point clusters include: the distance between the centroid of the change point cluster and the environmental data acquisition device, and the length of the main distribution direction axis of the change points in the change point cluster. Distribution characteristic conditions include: the distance between the centroid of the change point cluster and the environmental data acquisition device is less than a preset distance threshold, and the length of the main distribution direction axis of the change points in the change point cluster is greater than a preset length threshold. In this case, if the distance between the centroid of the change point cluster and the environmental data acquisition device is less than the preset distance threshold, and the length of the main distribution direction axis of the change points in the change point cluster is greater than the preset length threshold, it can be determined that the area corresponding to the online point cloud data has undergone environmental change. When the length of the main distribution direction axis of the change points in the change point cluster is greater than the preset length threshold, it indicates a high concentration of change points in the change point cluster, which is consistent with the occurrence of environmental change.

[0132] Here, the length of the principal distribution direction axis of the change points in the change point cluster is calculated based on the position information of the centroid of the change point cluster and the position information of each change point in the cluster. The length of the principal distribution direction axis of the change points in the change point cluster can be calculated using the following formula:

[0133]

[0134]

[0135] In the above formula, λ1 represents the length of the main distribution direction axis of the change points in the change point cluster, and λ2 represents the length of the secondary distribution direction axis of the change points in the change point cluster. The first axial coordinate represents the centroid of the changing point cluster. The second axial coordinate, x, represents the centroid of the changing point cluster. i This represents the first axial coordinate, y, of a change point within a change point cluster. i Let represent the axial coordinate of a change point in a change point cluster, n represent the number of change points in the change point cluster, and A is a pre-configured reference matrix.

[0136] In some embodiments, based on determining whether the area corresponding to the online point cloud data has undergone environmental changes based on the distance between the centroid of the change point cluster and the environmental data acquisition device, the distribution density of the change points in the change point cluster can be further introduced, and the environmental changes can be further determined based on the distribution density.

[0137] Specifically, the distribution characteristics of the change point clusters include: the distance between the centroid of the change point cluster and the environmental data acquisition device, and the distribution density of change points within the cluster. Distribution characteristic conditions include: the distance between the centroid of the change point cluster and the environmental data acquisition device is less than a preset distance threshold, and the distribution density of change points within the cluster is greater than a preset density threshold. In this case, if the distance between the centroid of the change point cluster and the environmental data acquisition device is less than the preset distance threshold, and the distribution density of change points within the cluster is greater than the preset density threshold, it can be determined that environmental changes have occurred in the area corresponding to the online point cloud data. When the distribution density of change points within the change point cluster is greater than the preset density threshold, it indicates a high concentration of change points within the cluster, which is consistent with the occurrence of environmental changes.

[0138] Here, the distribution density of change points in the change point cluster is calculated based on the number of change points in the change point cluster and the distribution area of ​​the change point cluster.

[0139] Optionally, the distribution area of ​​the change point cluster is the area of ​​an ellipse with the major axis of the main distribution direction of the change points in the cluster as its major axis and the minor axis of the secondary distribution direction of the change points in the cluster as its minor axis. The lengths of the major and secondary distribution direction axes are calculated based on the position information of the centroid of the change point cluster and the position information of each change point within the cluster.

[0140] The distribution area of ​​the change point cluster can be calculated using the following formula: S=π·λ1·γ2.

[0141] In the above formula, S represents the distribution area of ​​the change point cluster, π represents pi, λ1 represents the length of the main distribution direction axis of the change points in the change point cluster, and λ2 represents the length of the secondary distribution direction axis of the change points in the change point cluster.

[0142] After obtaining the distribution area of ​​the change point cluster, dividing the number of change points in the cluster by the distribution area yields the distribution density of the change points in the cluster. An ellipse constructed with the major distribution axis of the change points in the cluster as its major axis and the minor distribution axis as its minor axis can essentially encompass all the change points in the cluster. The distribution density determined based on the area of ​​this ellipse and the number of change points in the cluster can objectively reflect the concentration of the change points.

[0143] Based on the same principle as the environmental change detection method described above, this disclosure provides an environmental change detection device. Figure 4 A schematic diagram of the first environmental change detection device provided in this disclosure is shown. Figure 4As shown, the environmental change detection device 400 includes an online point cloud processing module 410, a map unit determination module 420, a change point generation module 430, and a change detection module 440.

[0144] The online point cloud processing module 410 is used to divide online point cloud data into multiple online data grids.

[0145] The map cell determination module 420 is used to determine the map cell corresponding to each online data raster from the offline map. The change point generation module 430 is used to calculate the height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding map cell for each online data raster, and generate the change point corresponding to the online data cell when the height difference is greater than the height difference threshold.

[0146] The change detection module 440 is used to calculate the distribution feature information for all change points. In response to the distribution feature information meeting the distribution feature conditions, it determines that the area corresponding to the online point cloud data has undergone environmental changes.

[0147] The environmental change detection device provided in this embodiment can rasterize offline maps and online point cloud data, compare differences on a grid-by-grid basis, initially screen out grids where environmental changes have occurred, and then determine the final environmental change result by analyzing the distribution of these grids. This process allows for the use of fewer computing resources to complete the detection process, eliminating the need to download online point cloud data locally, enabling the detection process to be performed online in real time, thus improving the efficiency of environmental change detection. Furthermore, the device provides high precision for detecting online point cloud data, which helps improve the accuracy of the environmental change detection results.

[0148] In this embodiment of the disclosure, the offline map includes multiple map data layers, each map data layer contains point cloud data of a corresponding height range in the offline map, and each map data layer has been divided into multiple offline point cloud grids;

[0149] The map cell determination module 420, when determining the map cell corresponding to each online data raster from the offline map, is specifically used for:

[0150] Based on the maximum height range of point clouds in online point cloud data, at least one target map data layer is determined from the offline map, wherein the maximum height range is within the total height range of at least one target map data layer;

[0151] Merge offline point cloud rasters with the same horizontal coordinates in at least one target map data layer into a single map unit;

[0152] Based on the horizontal coordinates of each online data raster, the map unit corresponding to each online data raster is determined.

[0153] In this embodiment of the disclosure, each map unit corresponding to an online data raster includes a first map unit and at least one second map unit;

[0154] The horizontal coordinates of the first map cell are the same as the horizontal coordinates of the online data raster, and the second map cell is within a preset distance range of the first map cell.

[0155] In this embodiment of the disclosure, the change point generation module 430, when calculating the height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding map unit for each online data raster, and generating a change point corresponding to the online data unit when it is determined that the height difference is greater than a height difference threshold, is specifically used for:

[0156] For each online data raster, calculate the first height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding first map unit, and the second height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding second map unit.

[0157] When the first height difference and each second height difference are both greater than the height difference threshold, change points corresponding to the online data units are generated.

[0158] In this embodiment of the disclosure, the coordinates of the change point corresponding to the online data unit are generated based on the horizontal coordinates of the online data raster.

[0159] In this embodiment of the disclosure, when the change detection module 440 calculates distribution feature information for all change points, and determines that the area corresponding to the online point cloud data has undergone environmental change in response to the distribution feature information meeting the distribution feature conditions, it is specifically used for:

[0160] Cluster all change points to obtain at least one change point cluster;

[0161] Calculate the distribution characteristics of each cluster of change points;

[0162] In response to determining that the distribution characteristic information of at least one changed point cluster meets the distribution characteristic conditions, it is determined that the region corresponding to the point cloud data associated with the changed point cluster in the online point cloud data has undergone environmental change.

[0163] In this embodiment of the disclosure, the distribution characteristic information of the change point cluster includes: the distance between the centroid of the change point cluster and the environmental data acquisition device;

[0164] The distribution characteristics include: the distance between the centroid of the change point cluster and the environmental data acquisition device is less than a preset distance threshold;

[0165] An environmental data acquisition device is a device used to acquire online point cloud data;

[0166] The distance between the centroid of the change point cluster and the environmental data acquisition device is calculated based on the location information of the centroid of the change point cluster and the location information of the environmental data acquisition device.

[0167] The location information of the centroid of the change point cluster is calculated based on the location information of the change points in the change point cluster.

[0168] In this embodiment of the disclosure, the position information of the centroid of the change point cluster includes the first axial coordinate and the second axial coordinate;

[0169] The first axial coordinate of the centroid of the change point cluster is the average of the first axial coordinates of all change points in the change point cluster.

[0170] The second axial coordinate of the centroid of the change point cluster is the average of the second axial coordinates of all change points in the change point cluster.

[0171] In this embodiment of the disclosure, the distribution feature information of the change point cluster further includes: the length of the main distribution direction axis of the change points in the change point cluster;

[0172] The distribution characteristic conditions include: the length of the main distribution direction axis of the change points in the change point cluster is greater than the preset length threshold.

[0173] In this embodiment of the disclosure, the length of the main distribution direction axis is calculated based on the position information of the centroid of the change point cluster and the position information of each change point in the change point cluster.

[0174] In this embodiment of the disclosure, the distribution characteristic information of the change point cluster further includes: the distribution density of change points in the change point cluster;

[0175] The distribution characteristic conditions include: the distribution density of change points in the change point cluster is greater than the preset density threshold.

[0176] In this embodiment of the disclosure, the distribution density of change points in the change point cluster is calculated based on the number of change points in the change point cluster and the distribution area of ​​the change point cluster;

[0177] The distribution area of ​​the change point cluster is the area of ​​an ellipse with the main distribution direction axis of the change points in the change point cluster as the major axis and the secondary distribution direction axis of the change points in the change point cluster as the minor axis.

[0178] The lengths of the main distribution direction axis and the secondary distribution direction axis are calculated based on the position information of the centroid of the change point cluster and the position information of each change point in the change point cluster.

[0179] It is understood that the modules of the environmental change detection device in this embodiment have the function of implementing the corresponding steps of the environmental change detection method described above. This function can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions. These modules can be software and / or hardware, and each module can be implemented individually or multiple modules can be integrated. For a detailed description of the functions of each module of the environmental change detection device, please refer to the corresponding description of the environmental change detection method described above; further details will not be repeated here.

[0180] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0181] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, a computer program product, and an autonomous vehicle.

[0182] In an exemplary embodiment, the electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described in the above embodiments. The electronic device may be the computer or server described above.

[0183] In an exemplary embodiment, the readable storage medium may be a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method described in the above embodiments.

[0184] In an exemplary embodiment, the computer program product includes a computer program that, when executed by a processor, implements the method described in the above embodiments.

[0185] In an exemplary embodiment, the autonomous vehicle includes the aforementioned electronic device, which may specifically be an onboard computer.

[0186] Figure 5A schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0187] like Figure 5 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. The RAM 503 may also store various programs and data required for the operation of the device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0188] Multiple components in electronic device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0189] The computing unit 501 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the environmental change detection method. For example, in some embodiments, the environmental change detection method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the environmental change detection method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the environmental change detection method by any other suitable means (e.g., by means of firmware).

[0190] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0191] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0192] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0193] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0194] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0195] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0196] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0197] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for detecting environmental change, the method comprising: The online point cloud data is divided into multiple online data rasters; Determine the map unit corresponding to each of the online data rasters from the offline map; For each of the online data rasters, the height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding map unit is calculated. When it is determined that the height difference is greater than the height difference threshold, a change point corresponding to the online data raster is generated. Calculating distribution feature information for all the change points, and in response to the distribution feature information meeting the distribution feature conditions, determining that the region corresponding to the online point cloud data has undergone environmental change; including: clustering all the change points to obtain at least one change point cluster; calculating distribution feature information for each change point cluster; and in response to determining that the distribution feature information of at least one change point cluster meets the distribution feature conditions, determining that the region corresponding to the point cloud data associated with the change point cluster in the online point cloud data has undergone environmental change. The distribution characteristic information of the change point cluster also includes: the length of the main distribution direction axis of the change points in the change point cluster; The distribution characteristic conditions include: the length of the main distribution direction axis of the change points in the change point cluster is greater than a preset length threshold.

2. The method according to claim 1, wherein, The offline map includes multiple map data layers, each map data layer contains point cloud data of a corresponding height range in the offline map, and each map data layer has been divided into multiple offline point cloud grids; The step of determining the map unit corresponding to each of the online data rasters from the offline map includes: Based on the maximum height range of the point cloud in the online point cloud data, at least one target map data layer is determined from the offline map, wherein the maximum height range is within the total height range of the at least one target map data layer; The offline point cloud grids with the same horizontal coordinates in at least one target map data layer are merged into a map unit; Based on the horizontal coordinates of each online data raster, the map unit corresponding to each online data raster is determined.

3. The method according to claim 1, wherein, Each of the online data graticules corresponds to a map unit including a first map unit and at least one second map unit; The horizontal coordinates of the first map unit are the same as the horizontal coordinates of the online data raster, and the second map unit is within a preset distance range of the first map unit.

4. The method according to claim 3, wherein, For each online data raster, the step of calculating the height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding map unit, and generating a change point corresponding to the online data raster when the height difference is determined to be greater than a height difference threshold, includes: For each of the online data rasters, a first height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding first map unit, and a second height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding second map unit are calculated. When it is determined that both the first height difference and each of the second height differences are greater than the height difference threshold, a change point corresponding to the online data raster is generated.

5. The method according to claim 4, wherein, The coordinates of the change points corresponding to the online data raster are generated based on the horizontal coordinates of the online data raster.

6. The method according to claim 1, wherein the distribution characteristic information of the change point cluster includes: The distance between the centroid of the change point cluster and the environmental data acquisition device; The distribution characteristic conditions include: the distance between the centroid of the change point cluster and the environmental data acquisition device is less than a preset distance threshold; The environmental data acquisition device is a device used to acquire the online point cloud data; The distance between the centroid of the change point cluster and the environmental data acquisition device is calculated based on the location information of the centroid of the change point cluster and the location information of the environmental data acquisition device. The position information of the centroid of the change point cluster is calculated based on the position information of the change points in the change point cluster.

7. The method according to claim 6, wherein, The position information of the centroid of the change point cluster includes the first axial coordinate and the second axial coordinate; The first axial coordinate of the centroid of the change point cluster is the average of the first axial coordinates of all change points in the change point cluster. The second axial coordinate of the centroid of the change point cluster is the average of the second axial coordinates of all change points in the change point cluster.

8. The method according to claim 1, wherein, The length of the main distribution direction axis is calculated based on the position information of the centroid of the change point cluster and the position information of each change point in the change point cluster.

9. The method according to claim 1, wherein, The distribution characteristic information of the change point cluster also includes: the distribution density of change points in the change point cluster; The distribution characteristic conditions include: the distribution density of change points in the change point cluster is greater than a preset density threshold.

10. The method according to claim 9, wherein, The distribution density of change points in the change point cluster is calculated based on the number of change points in the change point cluster and the distribution area of ​​the change point cluster. The distribution area of ​​the change point cluster is the area of ​​an ellipse with the main distribution direction axis of the change points in the change point cluster as the major axis and the secondary distribution direction axis of the change points in the change point cluster as the minor axis. The lengths of the main distribution direction axis and the sub-distribution direction axis are calculated based on the position information of the centroid of the change point cluster and the position information of each change point in the change point cluster.

11. An environmental change detection device, the device comprising: The online point cloud processing module is used to divide online point cloud data into multiple online data rasters; A map cell determination module is used to determine the map cell corresponding to each of the online data gratings from the offline map; The change point generation module is used to calculate the height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding map unit for each online data raster, and generate a change point corresponding to the online data raster when it is determined that the height difference is greater than the height difference threshold. The change detection module is used to calculate the distribution feature information for all the change points, and in response to the distribution feature information meeting the distribution feature conditions, determine that the area corresponding to the online point cloud data has undergone environmental change; specifically, it is used to: cluster all the change points to obtain at least one change point cluster; and calculate the distribution feature information for each change point cluster. In response to determining that the distribution feature information of at least one of the changed point clusters meets the distribution feature conditions, it is determined that the region corresponding to the point cloud data associated with the changed point cluster in the online point cloud data has undergone environmental change; The distribution characteristic information of the change point cluster also includes: the length of the main distribution direction axis of the change points in the change point cluster; The distribution characteristic conditions include: the length of the main distribution direction axis of the change points in the change point cluster is greater than a preset length threshold.

12. The apparatus according to claim 11, wherein, The offline map includes multiple map data layers, each map data layer contains point cloud data of a corresponding height range in the offline map, and each map data layer has been divided into multiple offline point cloud grids; When determining the map unit corresponding to each online data raster from the offline map, the map unit determination module is specifically used for: Based on the maximum height range of the point cloud in the online point cloud data, at least one target map data layer is determined from the offline map, wherein the maximum height range is within the total height range of the at least one target map data layer; The offline point cloud grids with the same horizontal coordinates in at least one target map data layer are merged into a map unit; Based on the horizontal coordinates of each online data raster, the map unit corresponding to each online data raster is determined.

13. The apparatus according to claim 11, wherein, Each of the online data graticules corresponds to a map unit including a first map unit and at least one second map unit; The horizontal coordinates of the first map unit are the same as the horizontal coordinates of the online data raster, and the second map unit is within a preset distance range of the first map unit.

14. The apparatus according to claim 13, wherein, The change point generation module, when calculating the height difference between the point cloud height value of each online data raster and the point cloud height value of the corresponding map unit, and generating a change point corresponding to the online data raster when the height difference is greater than a height difference threshold, specifically performs the following: For each of the online data rasters, a first height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding first map unit, and a second height difference between the point cloud height value of the online data raster and the point cloud height value of the corresponding second map unit are calculated. When it is determined that both the first height difference and each of the second height differences are greater than the height difference threshold, a change point corresponding to the online data raster is generated.

15. An electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method according to any one of claims 1-10.

16. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method according to any one of claims 1-10.

17. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-10.

18. An autonomous vehicle comprising the electronic device of claim 15.

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