Map updating method and device and readable storage medium

Abstract

Embodiments of the present application provide a map updating method and device and a readable storage medium, relating to the technical field of mapping. In the method, during the mapping process of a robot, in response to a change amount of a target attitude angle of an environmental sensor of the robot from a first time to a second time exceeding a preset change amount threshold, the data sensed by the environmental sensor at the second time is determined as abnormal data, the second time is later than the first time, and the target attitude angle includes a pitch angle or a roll angle. An abnormal period is determined according to the second time and a target time length. The data detected by the environmental sensor in the abnormal period is determined as abnormal data. A display device is prohibited from displaying data related to the abnormal data. In this way, by prohibiting the display device from displaying the data related to the abnormal data, the map can be prevented from being contaminated by the abnormal data, and the quality of the map can be improved to some extent.

Description

Technical Field

[0001] This invention relates to the field of mapping technology, and in particular to a map updating method, apparatus, and readable storage medium. Background Technology

[0002] Typically, robots collect data from the physical environment using onboard environmental sensors. Based on this data, they build maps of the physical environment for later use. For example, navigation, path planning, and localization can then be performed based on these maps.

[0003] In related technologies, during the mapping process, data related to the data perceived by environmental sensors is often displayed indiscriminately, that is, the map is continuously updated based on the data perceived by environmental sensors. This approach leads to poor map quality. Summary of the Invention

[0004] This application provides a map updating method, apparatus, and readable storage medium to solve the problem of poor map quality.

[0005] In a first aspect, embodiments of this application disclose a map updating method, the method comprising: During the robot mapping process, if the change in the target attitude angle of the robot's environmental sensor from the first moment to the second moment exceeds a preset change threshold, the data sensed by the environmental sensor at the second moment is determined to be abnormal data. The second moment is later than the first moment. The target attitude angle includes pitch angle or roll angle. Based on the second time point and the target duration, the abnormal period is determined; The data detected by the environmental sensors during the abnormal period was determined to be abnormal data. The display device is prohibited from displaying data related to the abnormal data.

[0006] This application also discloses a map updating device, which includes a memory and a processor; The memory is used to store program code; The processor calls the program code, which, when executed, performs the following operations: During the robot mapping process, if the change in the target attitude angle of the robot's environmental sensor from the first moment to the second moment exceeds a preset change threshold, the data sensed by the environmental sensor at the second moment is determined to be abnormal data. The second moment is later than the first moment. The target attitude angle includes pitch angle or roll angle. Based on the second time point and the target duration, the abnormal period is determined; The data detected by the environmental sensors during the abnormal period was determined to be abnormal data. The display device is prohibited from displaying data related to the abnormal data.

[0007] This application also discloses a readable storage medium storing instructions that, when executed by one or more processors, cause the processors to perform the methods described above.

[0008] This application also discloses a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0009] The embodiments of this application include the following advantages: In the map update method provided in this application embodiment, during the robot mapping process, in response to the change in the target attitude angle of the robot's environmental sensors from a first moment to a second moment exceeding a preset change threshold, the data perceived by the environmental sensors at the second moment is determined to be abnormal data. The second moment is later than the first moment, and the target attitude angle includes pitch angle or roll angle. An abnormal time period is determined based on the second moment and the target duration. The data detected by the environmental sensors within the abnormal time period is determined to be abnormal data. The display device is prohibited from displaying data related to the abnormal data. Thus, by dynamically determining whether to classify the data perceived by the environmental sensors as abnormal data based on the change in the target attitude angle of the robot's environmental sensors at different moments during the mapping process, and by determining that the data perceived by the environmental sensors at the second moment is abnormal data when the change in the target attitude angle exceeds a preset change threshold, and prohibiting the display device from displaying data related to the abnormal data, map contamination by abnormal data can be avoided, thereby improving map quality to a certain extent.

[0010] Furthermore, in this embodiment, in response to the change in the target attitude angle of the robot's environmental sensor from the first moment to the second moment exceeding the preset change threshold, the abnormal time period is further determined, and the data detected during the abnormal time period is directly determined as abnormal data. In this way, the data collected by the robot during the continuous abnormal event can be avoided from being used for map updates, which would lead to map pollution, and thus further improve map quality. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. The drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating the steps of a map updating method provided in an embodiment of this application; Figure 2 This application provides a schematic diagram of a system architecture. Figure 3 This is a block diagram of a map updating device provided in an embodiment of this application. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. The described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] First, an exemplary application scenario related to the embodiments of this application will be described. The map update method provided in the embodiments of this application can be applied to robot mapping scenarios. For example, the robot can be a wheeled robot, a legged robot, etc. To facilitate the robot's navigation, path planning, and other operations, the robot will perform Simultaneous Localization and Mapping (SLAM) in the physical environment. Specifically, the robot will build a map for the physical environment based on data perceived by environmental sensors. The environmental sensors can be sensors used to collect the data required for mapping; for example, the environmental sensors can be LiDAR, cameras, etc., and the data perceived by the environmental sensors can be point cloud data or images. The point cloud data can include laser frames, and one frame of point cloud data can correspond to a set of distance points collected by the LiDAR in one scanning cycle.

[0015] In related technologies, data perceived by environmental sensors at any given moment is indiscriminately displayed on the map, along with data related to that moment, thus updating the map. However, in real-world applications, robots may encounter events during movement that cause data anomalies (hereinafter referred to as anomaly events). Examples include laser strikes, uneven surfaces, and changes in robot pitch. When anomalies occur, the robot's posture often changes abruptly, leading to sudden changes in the data perceived by the environmental sensors. For instance, uneven ground or pitch changes might cause laser strikes, which could scan the ground or near-ground areas, resulting in anomalies in the point cloud structure. Directly displaying data related to these anomalies would negatively impact map quality, leading to poor map quality.

[0016] Therefore, this application provides a map updating method. The map updating method will be described in detail below.

[0017] Figure 1 This is a flowchart illustrating the steps of a map update method provided in an embodiment of this application, such as... Figure 1 As shown, the method may include: Step 101: During the robot mapping process, in response to the change in the target attitude angle of the robot's environmental sensor from the first moment to the second moment exceeding a preset change threshold, the data sensed by the environmental sensor at the second moment is determined to be abnormal data. The second moment is later than the first moment, and the target attitude angle includes pitch angle or roll angle.

[0018] Step 102: Determine the abnormal time period based on the second time and the target duration.

[0019] Step 103: Determine that the data detected by the environmental sensor during the abnormal period is abnormal data.

[0020] Step 104: Disable the display device from displaying data related to the abnormal data.

[0021] In this embodiment, the robot can begin mapping upon receiving a mapping instruction. For example, when the robot enters a physical environment, it can be controlled to use the current physical environment as the mapping environment and begin mapping. The mapping environment can be a home, hospital, shopping mall, factory, or similar environment. The data sensed by the environmental sensors is the data collected by the environmental sensors. The first moment and the second moment can be the timestamps of any two frames of data collected by the environmental sensors during the mapping process. In another implementation, the first moment and the second moment can be the timestamps of two consecutively collected frames of data; in yet another implementation, they can be the timestamps of two non-consecutively collected frames of data. This embodiment does not impose any limitations on these limitations.

[0022] In one implementation, the target attitude angle can be the pitch angle of the robot's environmental sensor. Correspondingly, the change in the target attitude angle can be the pitch angle change. This change in the target attitude angle represents the absolute value of the difference between the pitch angle of the robot's environmental sensor at a first moment and at a second moment. A preset change threshold can be a preset pitch angle change threshold. The pitch angle change threshold can be preset by a technician, and this embodiment does not impose any limitations on this. Alternatively, in another implementation, the target attitude angle can also be the roll angle of the robot's environmental sensor. Correspondingly, the change in the target attitude angle can be the roll angle change. This change in the target attitude angle represents the absolute value of the difference between the roll angle of the robot's environmental sensor at a first moment and at a second moment. A preset change threshold can be a preset roll angle change threshold. The pitch angle change threshold can be preset by a technician, and this embodiment does not impose any limitations on this.

[0023] When a robot's posture suddenly changes, the pitch or roll angle changes of the robot's environmental sensors will also change. For example, in one scenario, the physical environment contains raised terrain (e.g., speed bumps) or recessed terrain. When the robot traverses this raised terrain, it experiences an uphill and then downhill process; or, when the robot moves uphill from a slope to a higher plane, it experiences an uphill process, and when it moves downhill from a slope to a lower plane, it experiences a downhill process. Both of these will cause changes in the pitch angle of the robot's environmental sensors at the two moments. In another scenario, if when the robot traverses this raised terrain, one of its wheels is on a higher plane, causing the robot to tilt to one side—for example, one wheel is on the road surface and the other wheel is on the curb—the robot will tilt to the side on the road surface. This will cause changes in the roll angle sensed by the robot's environmental sensors at the two moments.

[0024] If the change in the target attitude angle exceeds a preset threshold, i.e., the change in pitch angle exceeds a preset threshold, or the change in roll angle exceeds a preset threshold, it indicates a significant change in the target attitude angle, a sudden change in the robot's attitude, or situations such as the robot hitting the ground with its laser. In this case, the sensed data is abnormal. Accordingly, in this embodiment, the data sensed by the environmental sensor at the second moment is directly identified as abnormal data, and the display device is prohibited from displaying data related to the abnormal data. For example, data related to the abnormal data may not be sent to the display device, thereby controlling the display device not to display data related to the abnormal data.

[0025] The display device can be a mobile terminal with display function connected to the robot, or it can be a display device integrated into the robot body; this application embodiment does not limit this. Further, abnormal data can be point cloud data sensed by the LiDAR, and data related to the point cloud data can be data generated based on the point cloud data that can be updated to the map. For example, taking a raster map as an example, the data related to the point cloud data can be occupancy probability statistics; taking a point cloud map as an example, the data related to the point cloud data can be points after downsampling and filtering. Abnormal data can be images sensed by the camera, and data related to the image can be data generated based on the image that can be updated to the map. For example, the data related to the image can be geometric information mentioned from the image.

[0026] In real-world applications, when a robot experiences an anomaly, it often takes a considerable amount of time to recover to its normal state. For example, when a robot traverses uneven terrain, moves uphill or downhill, it typically takes some time to return to a normal plane. In other words, for a period of time after the change in the target attitude angle exceeds a preset threshold, the data sensed by the environmental sensors often exhibits anomalies. Therefore, in this embodiment, the abnormal time period can be determined in response to the change in the target attitude angle of the robot's environmental sensors exceeding a preset threshold from a first moment to a second moment.

[0027] Specifically, the second moment can be taken as the start point of the time period, the sum of the second moment and the target duration can be taken as the end point of the time period, and (time period start point, time period end point) can be taken as the abnormal time period. Assume the second moment is... The target duration is Then the end point of the time period is An abnormal period can be considered a suppression window, which can be maintained by the suppression window control module. Specifically, within the suppression window, even if the change in the target attitude angle detected at a certain moment does not exceed a preset change threshold, the data collected at that moment will be identified as abnormal data. That is, all data detected within the abnormal period will be identified as abnormal data, and the display device will be prohibited from displaying data related to the abnormal data.

[0028] The target duration can be preset, and this application embodiment does not limit this. For example, the duration of abnormal events can be pre-calculated, and the average duration can be used as the target duration. In one application scenario, the target duration can be 2 seconds. In another implementation, the target duration can also be determined based on the robot's travel speed. The higher the travel speed, the longer the target duration, thus extending the suppression window when the robot is moving at high speed and shortening the suppression window when it is moving at low speed or stationary, thereby improving the accuracy of abnormal periods.

[0029] In some embodiments, the target duration can also be determined based on road surface condition information in the direction of travel, wherein the road surface condition information indicates at least one of the following: the degree, number, and size of depressions and / or protrusions on the road surface in the direction of travel. For example, the greater the degree of depressions and / or protrusions, the longer the target duration; the more depressions and / or protrusions, the longer the target duration; and the larger the size of depressions and / or protrusions, the longer the target duration. For example, an image of the road surface can be acquired and input into a large model to identify the degree, number, and size of depressions and / or protrusions. For example, if a speed bump is identified on the road surface, the target duration can be determined to be 2 seconds; if three consecutive speed bumps are identified on the road surface, the target duration can be determined to be 6 seconds or 8 seconds.

[0030] In summary, the map update method provided in this application, during robot mapping, responding to the change in the target attitude angle of the robot's environmental sensors from a first moment to a second moment exceeding a preset change threshold, determines that the data perceived by the environmental sensors at the second moment is abnormal data. The second moment is later than the first moment, and the target attitude angle includes pitch or roll angles. Display devices are prohibited from displaying data related to the abnormal data. Thus, by dynamically determining whether to classify the data perceived by the environmental sensors as abnormal data based on the change in the target attitude angle of the robot's environmental sensors at different moments during mapping, and by determining that the data perceived by the environmental sensors at the second moment is abnormal data when the change in the target attitude angle exceeds a preset change threshold, and prohibiting the display devices from displaying data related to the abnormal data, map contamination by abnormal data can be avoided, thereby improving map quality.

[0031] Furthermore, in this embodiment, in response to the change in the target attitude angle of the robot's environmental sensor from the first moment to the second moment exceeding the preset change threshold, the abnormal time period is further determined, and the data detected during the abnormal time period is directly determined as abnormal data. In this way, the data collected by the robot during the continuous abnormal event can be avoided from being used for map updates, which would lead to map pollution, and thus further improve map quality.

[0032] Optionally, the embodiments of this application may further include the following steps: Step S21: In response to the change in the target attitude angle of the environmental sensor from the third time to the fourth time exceeding the preset change threshold, and the fourth time not being located in the abnormal time period, a new abnormal time period is determined for the robot based on the fourth time and the target duration.

[0033] The third and fourth timestamps can be the timestamps of any two frames of data collected by environmental sensors during the mapping process. Both the third and fourth timestamps are later than the second timetamp, and the fourth timetamp is later than the third timetamp. In one implementation, the third and fourth timestamps can be the timestamps of two consecutively collected frames of data; in another implementation, the third and fourth timestamps can be the timestamps of two non-consecutively collected frames of data. This application does not impose any restrictions on this.

[0034] The change in the target attitude angle detected by the environmental sensor from time 3 to time 4 exceeds a preset threshold, and since time 4 is not within an abnormal time period, this indicates that an abnormal event has re-emerged after the most recently identified abnormal time period ended. Therefore, the data sensed by the environmental sensor at time 4 can be identified as abnormal data, and a new abnormal time period can be determined for the robot based on time 4 and the target duration. Specifically, time 4 can be taken as the new start point of the time period, and the sum of time 4 and the target duration can be taken as the new end point of the time period. The sum of the new start point and the new end point can be taken as the new abnormal time period. Accordingly, the data detected within the new abnormal time period can be identified as abnormal data.

[0035] During the duration of the previous abnormal event, i.e., within the previously identified abnormal period, the change in the target attitude angle of the robot's environmental sensors often exceeds a preset threshold. If the operation to determine a new abnormal period is repeatedly triggered within the previously identified abnormal period, the abnormal period will be continuously extended, leading to the problem of normal data being misjudged as abnormal data. Furthermore, with logs generated for each trigger, repeated triggering can cause log overload. For example, suppose the previously determined abnormal time period was (15:00:00 on June 1, 2026, 15:00:02 on June 1, 2026). A new abnormal event occurred at 15:00:05 on June 1, 2026, meaning that the period from 15:00:02 to 15:00:05 on June 1, 2026 was normal. If the operation to determine a new abnormal time period is triggered again within the previously determined abnormal time period, for example, if the operation to determine a new abnormal time period is triggered again at 15:00:01 on June 1, 2026, and the new abnormal time period is (15:00:01 on June 1, 2026, 15:00:03 on June 1, 2026), then the data detected between 15:00:02 and 15:00:03 on June 1, 2026 will be misjudged as abnormal data.

[0036] In this embodiment, a new abnormal time period is determined for the robot only when the change in the target attitude angle of the environmental sensor from the third time to the fourth time exceeds a preset change threshold and the fourth time is not located in an abnormal time period. This avoids the problem of repeatedly triggering the operation to determine a new abnormal time period within the previously determined abnormal time period, which would lead to the abnormal time period being continuously extended and normal data being misjudged as abnormal data.

[0037] Furthermore, by avoiding repeated triggering, the problem of log overload can also be avoided.

[0038] Optionally, the embodiments of this application may also include the following steps: Step S31, when the environmental sensor includes a lidar, based on the point cloud data collected by the lidar at the first moment and the point cloud data collected by the lidar at the second moment, determine that the change in the target attitude angle of the lidar from the first moment to the second moment exceeds a preset change threshold.

[0039] The lidar can be a single-line lidar or a multi-line lidar. The robot actively emits lasers using the lidar and measures the reflected laser light to calculate the distance to objects in the physical environment. Based on the calculated distances and the lidar's angle information at the time of laser emission, point cloud data is generated. The point cloud data collected at a given moment is called the point cloud frame for that moment, including information about all points collected by the lidar at that moment. In the event of an abnormal event, where the change in the target's attitude angle exceeds a preset threshold, the structure observed by the lidar will suddenly change. For example, the robot should normally scan a wall or the edge of a passageway; an abnormal event might cause the robot to suddenly scan the ground, the sky, near-ground areas, or unstable edges. This will cause a mismatch between the line features represented by the point cloud data collected at the first moment and the features represented by the point cloud data collected at the second moment. In other words, the change in the target's attitude angle from the first moment to the second moment will affect the point cloud data collected at both moments. Therefore, in this embodiment of the application, the point cloud data collected at these two moments can be used as a measure of the degree of change in the target attitude angle. By combining the point cloud data collected by the lidar at the first moment and the point cloud data collected by the lidar at the second moment, it can be determined whether the change in the target attitude angle exceeds the preset change threshold, thus ensuring the accuracy of the judgment operation.

[0040] Optionally, in one implementation, a first point cloud surface corresponding to the first time and a second point cloud surface corresponding to the second time can be determined based on the point cloud data acquired at the first time and the point cloud data acquired at the second time, respectively. The target attitude angle is then determined based on the first and second point cloud surfaces. For example, the target attitude angle is determined by calculating the angle between the normal vector of the first point cloud surface and the normal vector of the second point cloud surface. For example, plane fitting can be performed on the point cloud data acquired at the first time and the point cloud data acquired at the second time to obtain the ground plane vectors corresponding to each of the two frames of point cloud data. Here, the ground plane vector refers to the normal vector of the plane model fitted based on the point cloud data. For example, for any frame of point cloud data, three points can be randomly selected from the point cloud data to construct a plane model. For each plane model, the number of points in the point cloud data belonging to the plane represented by the plane model is calculated, which is taken as the number of interior points corresponding to the plane model. The normal vector of the plane model with the most interior points is calculated and taken as the ground plane vector corresponding to the point cloud data. Next, the target attitude angles corresponding to the two frames of point cloud data can be calculated based on the ground plane vectors corresponding to each frame. For example, for any frame of point cloud data, the front-to-back component (nx), left-to-right component (ny), and vertical component (nz) of the ground plane vector corresponding to the point cloud data can be calculated. The left-to-right component (ny) and vertical component (nz) are input into the atan2 function to obtain the roll angle corresponding to the point cloud data. Based on the left-to-right component (ny) and vertical component (nz), the projection length of the ground plane vector on the YZ plane is calculated. The negative of the front-to-back component (nx) and the projection length are input into the atan2 function to obtain the pitch angle corresponding to the point cloud data. Furthermore, if the difference between the target attitude angles corresponding to the two frames of point cloud data exceeds a preset change threshold, it is determined that the change in the target attitude angle of the lidar from the first moment to the second moment exceeds the preset change threshold. In this implementation, plane fitting is performed on point cloud data, and the ground plane vector corresponding to each point cloud data is calculated. Based on the ground plane vectors corresponding to the two frames of point cloud data, the target attitude angle corresponding to the two frames of point cloud data can be calculated. This provides a basis for judging whether the change in the target attitude angle of the lidar from the first moment to the second moment exceeds the preset change threshold, thus facilitating the judgment operation.

[0041] In another implementation, an attitude angle change prediction model can be pre-trained using a first training sample. The first training sample can include sample point cloud pairs and actual changes. The sample point cloud pairs consist of point cloud data collected by the LiDAR at two different time points, and the actual changes are the actual changes in the target attitude angle when the LiDAR collects the sample point cloud pair. The sample point cloud pairs can be input into the prediction model to be trained. Based on the output of the prediction model and the actual changes in the first training sample, a model loss value is calculated. The model parameters are optimized based on the model loss value until the model converges, obtaining the final attitude angle change prediction model. Correspondingly, the point cloud data collected by the LiDAR at the first time point and the point cloud data collected at the second time point can be used as a sample pair to be predicted and input into the attitude angle change prediction model. The output of this model is taken as the change in the target attitude angle of the LiDAR from the first time point to the second time point. If the model output value exceeds a preset change threshold, it can be determined that the change in the target attitude angle of the LiDAR from the first time point to the second time point exceeds the preset change threshold. In this way, by using model prediction, the change in the target attitude angle from the first moment to the second moment can be easily obtained by the lidar, which can improve the judgment efficiency to a certain extent.

[0042] Alternatively, the embodiments of this application may also include the following steps: Step S32: If the environmental sensor includes a camera, based on the image captured by the camera at the first moment and the image captured by the camera at the second moment, determine that the change in the target attitude angle of the camera from the first moment to the second moment exceeds a preset change threshold.

[0043] In the event of an abnormal event, specifically when the change in the target attitude angle exceeds a preset threshold, the structure in the image captured by the camera will also suddenly change. That is, the change in the target attitude angle from the first moment to the second moment will affect the images captured at those two moments. Therefore, in this embodiment, the images captured at the two moments can be used as a measure of the degree of change in the target attitude angle. By combining the image captured by the camera at the first moment and the image captured by the camera at the second moment, it is determined whether the change in the target attitude angle exceeds the preset threshold, thus ensuring the accuracy of the judgment operation to a certain extent.

[0044] For example, in one implementation, ground feature points can be extracted from the images acquired at the first and second time points, respectively. For any image, plane fitting is performed based on the ground feature points of that image to obtain the ground plane corresponding to that image. Then, the normal vector of the ground plane is calculated as the ground plane vector corresponding to that image. Next, the target attitude angles corresponding to the two frames of images can be calculated based on the ground plane vectors corresponding to the two frames of images. The specific calculation method can refer to the aforementioned implementation method of calculating the target attitude angles corresponding to the two frames of point cloud data based on the ground plane vectors corresponding to the two frames of point cloud data, which will not be repeated here. Furthermore, if the difference between the target attitude angles corresponding to the two frames of images exceeds a preset change threshold, it is determined that the change in the target attitude angle of the camera from the first time point to the second time point exceeds the preset change threshold. In this implementation, plane fitting is performed using ground feature points in the image, and the ground plane vector corresponding to each image is calculated. Based on the ground plane vectors corresponding to each of the two frames, the target attitude angle corresponding to each of the two frames can be calculated. This provides a basis for judging whether the change in the target attitude angle of the camera from the first moment to the second moment exceeds the preset change threshold, thus conveniently realizing the judgment operation.

[0045] In another implementation, a pose angle change prediction model can be pre-trained using a second training sample. The second training sample can include sample image pairs and actual changes. The sample image pairs consist of images captured by the camera at two different time points, and the actual changes are the actual changes in the target pose angle when the camera captures the sample image pair. The sample image pairs can be input into the prediction model to be trained. Based on the output of the prediction model and the actual changes in the second training sample, the model loss value is calculated. The model parameters are optimized based on the model loss value until the model converges, obtaining the final pose angle change prediction model. Correspondingly, the image captured by the camera at the first time point and the image captured by the camera at the second time point can be used as a prediction sample pair and input into the pose angle change prediction model. The output of this model is taken as the change in the target pose angle of the camera from the first time point to the second time point. If the model output value exceeds a preset change threshold, it can be determined that the change in the target pose angle of the camera from the first time point to the second time point exceeds the preset change threshold. In this way, the change in the target pose angle of the camera from the first time point to the second time point can be conveniently obtained through model prediction, thereby improving the judgment efficiency to a certain extent.

[0046] Optionally, the step of determining that the data sensed by the environmental sensor at the second time is abnormal data in response to the change in the target attitude angle of the robot's environmental sensor from the first time moment to the second time moment exceeding a preset change threshold may specifically include: Step 1011: In response to the robot not being in a yaw motion state and the change in the target attitude angle exceeding the preset change threshold, determine that the data sensed by the environmental sensor at the second moment is abnormal data.

[0047] In this context, "the robot is in a yaw motion" refers to the robot turning in place. When a robot is in a yaw motion, the perspective of the environmental sensors changes rapidly, affecting the data collected at different times. This can lead to misjudgments when determining whether the change exceeds a preset threshold based on the collected point cloud data or images.

[0048] Therefore, in this embodiment, if the change in the target attitude angle exceeds a preset threshold when the robot is not in a yaw motion state, the data sensed by the environmental sensor at the second moment is determined to be abnormal data. This avoids the problem of the environmental sensor misjudging data as abnormal due to robot rotation, thereby improving the accuracy of data processing.

[0049] Optionally, the embodiments of this application may further include the following steps: Step S41: When the robot is in a yaw motion state, determine that the data sensed by the environmental sensor is normal data.

[0050] Step S42: Control the display device to display data related to the normal data.

[0051] In this embodiment, if the robot is in a yaw motion state, the data sensed by the environmental sensors can be directly determined as normal data without needing to identify whether the change in the target attitude angle of the robot's environmental sensors from the first moment to the second moment exceeds a preset change threshold. Accordingly, the display device can be controlled to display data related to the normal data. For example, data related to the normal data can be sent to the display device normally, thereby controlling the display device to normally display the data related to the normal data.

[0052] In this embodiment, when the robot is in a yaw motion, the data sensed by the environmental sensors is determined to be normal data, and the display device is controlled to display data related to the normal data. This ensures that the displayed map is updated normally with data related to the normal data, thereby ensuring the integrity and accuracy of the created map.

[0053] Optionally, in this embodiment of the application, it can be determined whether the robot is in a yaw motion state by the following steps: Step S51: If the environmental sensor includes a lidar, determine the relative rotation angle from the first time moment to the second time moment based on the relative pose matrix of the point cloud data acquired by the lidar at the first time moment and the relative pose matrix of the point cloud data acquired by the lidar at the second time moment. Alternatively, if the environmental sensor includes a camera, determine the relative rotation angle from the first time moment to the second time moment based on the image acquired by the camera at the first time moment and the image acquired by the camera at the second time moment.

[0054] Step S52: When the relative rotation angle is greater than the preset rotation angle, determine that the robot is in a yaw motion state.

[0055] Step S53: If the relative rotation angle is not greater than the preset rotation angle, determine that the robot is not in a yaw motion state.

[0056] The relative pose matrix of the point cloud data is determined based on the pose matrix of the point cloud data in this frame and the pose matrix of the point cloud data in the previous frame. The relative pose matrix of the point cloud data is used to characterize the relative transformation from the point cloud data in this frame to the point cloud data in the previous frame.

[0057] The pose of point cloud data can include coordinates and heading angle, and the pose of point cloud data can be the odometry pose provided by the robot's odometry.

[0058] For any frame of point cloud data, the point cloud data acquisition time is... (Assuming this point cloud data is the point cloud data collected at the second time step, then the second time step is...) Then the pose of the point cloud data can be represented as ( , , The pose matrix of the point cloud data in this frame (i.e., the pose matrix of the point cloud data at the second time step) is:

[0059] Assume the previous frame of point cloud data was acquired at time (i.e., the first moment) when the current frame of point cloud data was acquired. Then the pose of the point cloud data can be represented as ( , , The pose matrix of the previous frame of point cloud data (i.e., the pose matrix of the point cloud data at the first moment) is:

[0060] Furthermore, the relative pose matrix of this frame of point cloud data can be:

[0061] The relative pose matrix of the point cloud data acquired at the first moment and the relative pose matrix of the point cloud data acquired at the second moment can be input into a preset relative rotation angle calculation function to obtain the relative rotation angle from the first moment to the second moment. This represents the relative pose matrix of the point cloud data acquired at the second time step, in... This represents the relative pose matrix of the point cloud data acquired at the first moment, and the relative rotation angle from the first moment to the second moment. It can be represented as:

[0062] In another implementation, feature point matching can be performed on the images acquired at the first and second time points. Based on the matching feature points in the image acquired at the first time point, the corresponding feature vector for that time point is calculated. Similarly, based on the matching feature points in the image acquired at the second time point, the corresponding feature vector for that time point is calculated. The angle between the feature vectors at the first and second time points is then used as the relative rotation angle from the first to the second time point. .

[0063] In another implementation, a rotation angle prediction model can be pre-trained using a third training sample. The second training sample can include sample image pairs and true rotation angles. The sample image pairs consist of images captured by the camera at two different time points, and the true rotation angle is the robot's actual rotation angle when the camera captures the sample image pair. The sample image pairs can be input into the rotation angle prediction model to be trained. Based on the output of the rotation angle prediction model and the true changes in the third training sample, a model loss value is calculated. The model parameters are optimized based on the model loss value until the model converges, obtaining the final rotation angle prediction model. Correspondingly, the image captured by the camera at the first time point and the image captured by the camera at the second time point can be used as a sample pair to be predicted and input into the rotation angle prediction model. The output of this model is used as the relative rotation angle from the first time point to the second time point. .

[0064] The preset rotation angle can be set according to actual conditions. For example, the maximum value of the relative rotation angle at two sampling moments when the robot is in a non-yaw motion state can be used as the preset rotation angle. This indicates a preset rotation angle, in one application scenario. It can be 0.8 degrees. If If so, the robot can be considered to be in a yaw motion state. If so, it can be considered that the robot is not in a yaw motion state.

[0065] In this embodiment, the relative pose matrix of the point cloud data acquired at the first moment and the relative pose matrix of the point cloud data acquired at the second moment, or the relative rotation angle from the first moment to the second moment, is determined based on the images acquired by the camera at the first moment and the images acquired by the camera at the second moment. Subsequently, it is only necessary to identify the relationship between the relative rotation angle and the preset rotation angle to determine whether the robot is in a yaw motion state. This ensures, to a certain extent, the efficiency of yaw motion state recognition.

[0066] Optionally, the embodiments of this application may further include the following steps: Step S61: Set an anomaly flag for the abnormal data.

[0067] Step S62: Identify key data from the data for which no abnormality marker has been set, and control the display device to display data related to the key data.

[0068] Step S63: Calculate the quality index of the data with the anomaly marker set, and obtain the quality index of the anomaly data.

[0069] The specific form of the anomaly marker can be set as needed, and this application embodiment does not limit this. For example, the anomaly marker can be a "hit_ground" marker. Anomaly markers can be set for point cloud data or images collected during the second time point and the abnormal period to facilitate subsequent differentiation between abnormal and normal data. Key data can be key frames selected from multiple frames of data collected from environmental sensors. During mapping, key frames can be used for map updates. In this application embodiment, the presence or absence of anomaly markers is used as a mapping gating control. Specifically, in the subsequent mapping process, key point cloud frames or key image frames are identified only from point cloud data or images without anomaly markers. The display device is controlled to display data related to the key point cloud frames or data related to the key image frames.

[0070] For example, when environmental sensors collect point cloud data, the point cloud data collected by the environmental sensors can be sent to a local matching module and a global matching module. The local matching module performs point cloud matching on this frame of point cloud data with previously identified key point cloud frames to obtain the quality index of this frame of point cloud data. This quality index can be the root mean square error, matching score, point cloud density, and residuals output by the matching algorithm, etc. For data with anomaly markers, the quality index of the anomaly data can be obtained through the matching algorithm. Further, the local matching module inserts the pose of this frame of point cloud data into a local optimization graph, where a node in the local optimization graph represents the pose of a frame of point cloud data. The pose of this frame of point cloud data is then locally optimized by a local optimizer.

[0071] If the received point cloud data has an anomaly flag, the global matching module discards the received point cloud data. If the received point cloud data does not have an anomaly flag, the module determines whether to select the point cloud data frame as a key point cloud frame based on the quality index of the frame, the robot's movement distance, and the preset key frame selection strategy. Further, after selecting a new key point cloud frame each time, the global matching module inserts the key point cloud frame into the global optimization graph, where a node in the global optimization graph represents the pose of a key point cloud frame. The global optimizer optimizes the pose of the key point cloud frames in the global optimization graph to maintain pose continuity. The local optimizer and the global optimizer can share some states, for example, sharing the pose of the point cloud frames. The control display device displays data related to normal data, specifically data related to key point cloud frames, where the key point cloud frames are obtained by filtering from normal data. For example, the key point cloud frames can be projected onto the global coordinate system based on their poses to update the occupancy probability of each grid cell and obtain occupancy probability statistics. The control display device displays the map according to the latest obtained occupancy probability statistics, thereby updating the displayed map.

[0072] When images are acquired by environmental sensors, the sharpness, contrast, and noise level of the image frame can be calculated as quality indicators. If no anomaly marker is set for the image frame, a decision is made based on the quality indicators, the robot's movement distance, and a preset keyframe selection strategy to determine whether to select the image frame as a key image frame. Each time a new key image frame is selected, geometric information is extracted from it. Based on the pose of the key image frame, the extracted geometric information is fused into the current map, and the display device is controlled to display it. Of course, optimization can also be performed on key image frames, and this embodiment of the invention does not limit this.

[0073] In this embodiment, anomaly markers are set for abnormal data. Subsequently, key data is identified only from data without anomaly markers, and the display device is controlled to display data related to the key data. This avoids the problem of abnormal data (i.e., data with anomaly markers) being identified as key data, thus polluting the map. Furthermore, by calculating the quality index of the data with anomaly markers, a quality index for abnormal data is obtained. This facilitates the subsequent selection of higher-quality abnormal data for further applications based on the quality index.

[0074] It should be noted that, in another implementation, abnormal data can also be discarded directly.

[0075] Optionally, this embodiment may further include: when the quality index of the abnormal data meets the preset index requirements, controlling the display device to display the robot's positioning information according to the pose information of the abnormal data.

[0076] The preset index requirements can be pre-set, and this application embodiment does not impose any limitations on this. Specifically, when environmental sensors collect point cloud data, the quality indicators may include root mean square error, matching score, point cloud density, and residual. Correspondingly, the preset index requirements may include a root mean square error less than a preset error threshold, a matching score greater than a preset score threshold, a point cloud density greater than a preset density threshold, and a residual less than a preset residual threshold. When environmental sensors collect images, the quality indicators may include sharpness, contrast, and noise level. Correspondingly, the preset index requirements may include sharpness greater than a preset sharpness threshold, contrast greater than a preset contrast threshold, and noise level less than a preset noise level threshold.

[0077] For any frame of normal data, the display device can be controlled to display the robot's positioning information according to the pose information of the normal data. Further, for abnormal data, if the quality indicators of the abnormal data frame meet preset requirements, it indicates that the abnormal data frame has a certain degree of usability. Accordingly, the pose of the abnormal data frame can be sent to the positioning module, which generates positioning information based on the pose of the abnormal data frame. This positioning information can be the global coordinates corresponding to the abnormal data frame. Then, the display device is controlled to display this positioning information.

[0078] In this embodiment, when the quality indicators of the abnormal data meet the preset requirements, the display device is controlled to display the robot's positioning information according to the pose information of the abnormal data. This can improve the continuity of the displayed positioning information, thereby improving the robot's positioning stability and positioning accuracy.

[0079] Optionally, in one implementation, determining that the change in the target attitude angle of the lidar from the first moment to the second moment exceeds a preset change threshold based on the point cloud data collected by the lidar at the first moment and the point cloud data collected by the lidar at the second moment may specifically include: Step S311: Determine the difference parameter between the first line segment in the point cloud data at the first moment and the second line segment in the point cloud data at the second moment of the lidar.

[0080] Step S312: In response to the absence of a second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold, determine that the change in the target attitude angle exceeds the preset change threshold.

[0081] In this implementation, the point cloud data at the second moment can be the current frame, and the point cloud data at the first moment can be the reference frame. The reference frame can be the point cloud data collected before the current frame, for example, it can be the point cloud data of the frame before the current frame. Figure 2 This application provides a system architecture diagram. It should be noted that after the LiDAR acquires a frame of point cloud data, the basic anomaly detection module first performs anomaly detection, such as detecting whether the frame of point cloud data has data interruptions, abnormal scanning frequency, or other basic anomalies. If a basic anomaly exists, the frame of point cloud data is discarded. If no basic anomaly exists, the laser distortion correction module uses odometry data to distort the frame of point cloud data and sends the distorted point cloud data to the detection module. The detection module calculates the difference parameters of line segments between the two frames of point cloud data to determine whether the change in the target attitude angle exceeds a preset change threshold. If the detection module receives only the first frame of point cloud data, it uses it as the reference frame. Upon receiving the next frame of point cloud data, it calculates the difference parameters between the two frames. If the robot is in a yaw motion, the operation of calculating the difference parameters to determine whether the change in the target attitude angle exceeds the preset change threshold is not performed. Instead, after the robot finishes rotating, the first frame of point cloud data received after the rotation ends is used as the new reference frame. Subsequently, the point cloud anomaly marking module can set anomaly marks for the abnormal data. The mapping module processes the point cloud data based on whether it has any anomaly markers, and then outputs the map.

[0082] The first line segment can be extracted from the reference frame, and the second line segment can be extracted from the current frame. The difference parameter measures the difference between the second and first line segments; the greater the difference, the larger the difference parameter. This difference parameter can include the direction difference, length difference, and midpoint distance between the line segments. If no abnormal event occurs at the second time step, the second line segment in the current frame will likely remain close to the first line segment in the reference frame, with a small difference parameter that does not exceed a preset threshold. If an abnormal event occurs at the second time step, it will cause a sudden change in the second line segment in the current frame, resulting in a significant difference from the first line segment in the reference frame. This will lead to a larger difference parameter that exceeds the preset threshold. Accordingly, if no second line segment exists whose difference parameter is less than the preset threshold, the change in the target attitude angle is considered to exceed a preset change threshold, indicating that an abnormal event has occurred. Conversely, if there is a second line segment whose difference parameter from the first line segment is not greater than the preset parameter threshold, then the point cloud data at the second moment can be considered to be close to the point cloud data at the first moment, and the change in the target attitude angle can be considered not to exceed the preset change threshold.

[0083] It should be noted that in another implementation, point cloud matching parameters can also be calculated between the point cloud data at the second time step and the point cloud data at the first time step. These parameters could include the Iterative Closest Point (ICP) matching rate, residual, and number of valid points. If there is no second line segment whose difference parameter from the first line segment is no greater than a preset threshold, and the point cloud matching parameters meet the preset requirements, then the point cloud data at the second time step is considered to match the point cloud data at the first time step. Correspondingly, it can be determined that the change in the target attitude angle does not exceed a preset change threshold. Thus, combining point cloud matching parameters for matching judgment improves the robustness of the solution.

[0084] In this embodiment, the difference parameter between a first line segment in the point cloud data at a first moment and a second line segment in the point cloud data at a second moment is determined. By simply identifying whether a second line segment exists whose difference parameter from the first line segment is not greater than a preset parameter threshold, it is possible to determine whether the change in the target attitude angle exceeds a preset change threshold. This ensures, to a certain extent, the efficiency of the determination.

[0085] In practical applications, due to cost, structural space, or power consumption limitations, some robots are not equipped with an Inertial Measurement Unit (IMU), making it impossible to determine whether an abnormal event has occurred based on IMU detection data. In such cases, directly using the collected point cloud data frames for map updates can lead to abnormal point cloud frames being selected as key point cloud frames, resulting in map contamination and a decrease in map quality. In this embodiment, without relying on IMU detection data, it is possible to determine whether the change in the target attitude angle exceeds a preset change threshold (i.e., whether an abnormal event has occurred) based on two frames of point cloud data. This solution is applicable to robots without an IMU, thus broadening its applicability. Furthermore, eliminating the need for IMU detection data reduces the requirement for installing, calibrating, and maintaining an IMU, thereby lowering platform hardware costs.

[0086] Optionally, this application embodiment may further include: when the robot is equipped with an inertial measurement unit, acquiring the detection data of the inertial measurement unit.

[0087] The step of determining that the change in the target attitude angle exceeds the preset change threshold in response to the absence of a second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold may specifically include: Step S312a: In response to the absence of a second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold, and the detection data indicating that the target posture angle of the robot is greater than a preset posture angle threshold, it is determined that the change in the target posture angle exceeds the preset change threshold.

[0088] The inertial measurement unit's (IMU) detection data can include acceleration components, and the robot's roll or pitch angle can be calculated based on the acceleration components at a second moment. If the robot's roll angle at the second moment is greater than a preset roll angle threshold, or if the robot's pitch angle at the second moment is greater than a preset pitch angle threshold, then the detection data indicates that the robot's target attitude angle is greater than a preset attitude angle threshold. Correspondingly, if the detection data indicates that the robot's target attitude angle is greater than a preset attitude angle threshold, it means that the IMU's detection data also indicates that an abnormal event has occurred in the robot.

[0089] Furthermore, the change in the target attitude angle can be determined to exceed a preset change threshold only if there is no second line segment whose difference parameter from the first line segment is no greater than a preset parameter threshold, and the detection data indicates that the robot's target attitude angle is greater than a preset attitude angle threshold. In this embodiment, when the robot is equipped with an inertial measurement unit (IMU), further combining the IMU's detection data to determine whether the change in the target attitude angle exceeds the preset change threshold can reduce the probability of misjudgment to some extent, thereby improving the accuracy of the judgment.

[0090] It should be noted that, in another implementation, a multi-source anomaly detection framework can be further formed by combining the robot's collision sensors or wheel speed anomaly detection mechanism. Specifically, the change in the target attitude angle is determined to exceed a preset change threshold only when there is no second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold, the detection data indicates that the robot's target attitude angle is greater than a preset attitude angle threshold, the collision sensor's detection value indicates that the robot has collided, and the wheel speed anomaly detection mechanism detects an abnormal robot wheel speed. This further reduces the probability of false positives.

[0091] Optionally, the second line segment in this embodiment of the application may have multiple segments, and may further include: Step S71: For each of the plurality of second line segments, in response to the difference parameter between the second line segment and the first line segment being less than the preset parameter threshold, the second line segment is regarded as a candidate line segment to obtain a plurality of candidate line segments.

[0092] Step S72: Based on the difference parameters between each of the multiple candidate line segments and the first line segment, determine the selection cost of each of the multiple candidate line segments.

[0093] Step S73: If the candidate line segment with the lowest cost is not selected to form a valid line segment pair with the first line segment, then the candidate line segment with the lowest cost is selected to form a valid line segment pair with the first line segment.

[0094] Step S74: In response to the number of valid line segment pairs being 0, determine that there is no second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold.

[0095] In this embodiment, for any first line segment, a second line segment whose difference parameter from the first line segment is no greater than a preset parameter threshold is selected as a candidate line segment for the first line segment. Next, the selection cost of each candidate line segment relative to the first line segment is calculated. The candidate line segment with the larger the difference parameter from the first line segment has, the greater its selection cost. Then, the selection costs of each candidate line segment are compared, and the candidate line segment with the smallest selection cost is selected as the target line segment for the first line segment. The existence of the target line segment in the established valid line segment pairs is checked. If it exists, it means that the target line segment is occupied by other first line segments and has formed a valid line segment pair with other first line segments. If it does not exist, it means that the target line segment is not occupied by other first line segments and has not formed a valid line segment pair with other first line segments. Accordingly, it can be considered that the target line segment and the first line segment can establish a line segment matching relationship, and the target line segment and the first line segment can be formed into a valid line segment pair. For example, the valid line segment pair can be added to the valid line segment matching set. .if If the value is empty, it means that the number of valid line segment pairs is 0.

[0096] In practical applications, when a robot moves normally, stable structures such as walls, door frames, and corridor boundaries observed in adjacent point cloud data will form corresponding line segments (referred to as stable line segments) in the point cloud data. Under normal circumstances, even if the robot translates or rotates at a small angle, the line segments in two frames of point cloud data tend to remain close in direction, length, and position. That is, the difference in direction, length, and midpoint distance between the line segments is often less than the corresponding preset parameter thresholds, and matching line segments often exist in the two frames of point cloud data. However, when an abnormal event occurs, the stable line segments in the point cloud data are disrupted. For example, if there is a table in front of the robot, the previously collected point cloud data will include a line segment corresponding to the table. If an abnormal event causes the robot to face the ground and collect a frame of point cloud data, the collected point cloud data will either lack the line segment corresponding to the table or the line segment corresponding to the table will be significantly disrupted. Correspondingly, if the number of valid line segment pairs is 0, it means that for all the first line segments in the point cloud data at the first time moment, there is no matching second line segment in the point cloud data at the second time moment. The point cloud data at the second time moment has undergone a sudden change relative to the point cloud data at the first time moment, and the correspondence of the continuous frame stable structure disappears. It can be determined that there is no second line segment whose difference parameter from the first line segment is not greater than the preset parameter threshold, so that the subsequent judgment detects an abnormal event at the second time moment. Compared with the method of judging whether an abnormal event has occurred by only using the distance threshold of a single frame, the scheme that relies solely on the distance threshold of a single frame is easily affected by wall distance, obstacles, reflective materials, and low objects, and has a high risk of false detection and false negative detection. In the embodiments of this application, abnormal events are identified by the correspondence of the continuous frame stable structure, which is closer to the actual changes in the mapping scene, thereby reducing the risk of false detection and false negative detection.

[0097] In this embodiment, for each of the multiple second line segments, in response to the difference parameter between the second line segment and the first line segment not being greater than a preset parameter threshold, the second line segment is designated as a candidate line segment to obtain multiple candidate line segments. Based on the difference parameters between each of the multiple candidate line segments and the first line segment, the selection cost of each of the multiple candidate line segments is determined. If the candidate line segment with the lowest selection cost does not form a valid line segment pair with other first line segments, the candidate line segment with the lowest selection cost is combined with the first line segment to form a valid line segment pair. Since abnormal events affect the number of valid line segment pairs between the point cloud data acquired at the first moment and the point cloud data acquired at the second moment, this embodiment counts the number of valid line segment pairs in these two frames of point cloud data. Based on the number of valid line segment pairs, and whether the number of valid line segment pairs is 0, it determines whether there is a second line segment whose difference parameter with the first line segment is not greater than a preset parameter threshold. This, to a certain extent, ensures the accuracy and rationality of the anomaly detection scheme.

[0098] It should be noted that, in one implementation, if the set of valid line segment matches is not empty, the confidence level of the point cloud data at the second time step can be calculated based on the selection cost corresponding to the candidate line segments in the set of valid line segment matches. For example, the mean selection cost can be calculated; the higher the mean selection cost, the lower the confidence level. Constraint weights are then set for the local optimizer and / or the global optimizer based on the confidence level, where a higher confidence level corresponds to a higher constraint weight.

[0099] Optionally, the method for determining the difference parameters between the first line segment in the point cloud data at the first moment and the second line segment in the point cloud data at the second moment includes: Step S311a: Based on the pose matrix of the point cloud data at the first time moment and the pose matrix of the point cloud data at the second time moment, determine the relative pose matrix of the point cloud data at the first time moment.

[0100] Step S311b: Compensate the endpoint coordinates of the second line segment based on the relative pose matrix.

[0101] Step S311c: Based on the endpoint coordinates of the first line segment and the compensated endpoint coordinates of the second line segment, determine the difference parameter between the first line segment and the second line segment.

[0102] In this embodiment, the wheeled odometer mounted on the robot can input the pose of point cloud data into the odometer cache module. The detection module can obtain the poses of the point cloud data at the first and second moments respectively from the odometer cache module based on the timestamps of the point cloud data at the first and second moments, and convert them into matrix form to obtain the pose matrices of the point cloud data at the first and second moments. This relative pose matrix can be as described above. .

[0103] Furthermore, the detection module can extract line segments from the point cloud data at the first time step using the line segment feature extraction submodule, and use the endpoint coordinates of the extracted line segments as the endpoint coordinates of the first line segment. Here, the endpoint coordinates of the line segment are the position coordinates of the line segment endpoints in the point cloud data. Line segments can be extracted from the point cloud data at the second time step, and the endpoint coordinates of the extracted line segments are used as the endpoint coordinates of the second line segment. The compensation and matching submodule performs endpoint coordinate compensation, and based on the endpoint coordinates of the first line segment and the compensated endpoint coordinates of the second line segment, determines the difference parameter between the first and second line segments, and determines the number of valid line segment pairs based on the difference parameter. The structural abrupt change judgment module, based on the number of valid line segment pairs, determines whether there is a structural abrupt change in consecutive frames, that is, whether there is a second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold.

[0104] by The coordinates of the endpoints of the second line segment can be compensated using the following formula:

[0105]

[0106] in, The matrix representation of the endpoint coordinates of the second line segment after compensation to the coordinate system of the previous frame can be used to... The coordinate values ​​included in the calculation are used as the endpoint coordinates of the compensated second line segment.

[0107] By compensating for the endpoint coordinates of the second line segment, the endpoint coordinates in the current frame are compensated to the coordinate system of the previous frame. This reduces the interference of the robot's normal translation and small-angle movement on line segment matching.

[0108] Next, for any one of the first and second line segments, the segment characteristics can be calculated based on the coordinates of its endpoints: segment length, segment direction angle, and midpoint coordinates. , Let represent the coordinates of the two endpoints of the line segment, then the length of the line segment can be: The line segment direction angle can be: The coordinates of the midpoint of this line segment can be: .

[0109] by The length of the first line segment is represented by... The length of the second line segment is represented by... Indicates the line segment direction angle of the first line segment, with Indicates the line segment direction angle of the second line segment, with Indicate the coordinates of the midpoint of the first line segment, using Let the coordinates of the midpoint of the second line segment be given. Then the length difference between the first and second line segments can be... The directional difference between the first line segment and the second line segment can be The distance between the midpoints of the first and second line segments can be expressed as the Euclidean distance between them. .

[0110] Accordingly, the aforementioned preset parameter threshold may include a preset length difference threshold. Directional difference threshold and the distance from the midpoint The difference parameter not being greater than the preset parameter threshold means , and Among them, the length difference threshold Directional difference threshold and the distance from the midpoint The specific value can be preset, and this application embodiment does not limit this. For example, the length difference threshold... Directional difference threshold and the distance from the midpoint They can be respectively , radian, In this embodiment, candidate line segments can be identified based on the difference parameters of line segment time and preset parameter thresholds without relying on IMU detection data or single-frame distance thresholds, thus reducing the limitations of the solution.

[0111] In this embodiment, the relative pose matrix of the point cloud data at the first moment is determined based on the pose matrix of the point cloud data at the first moment and the pose matrix of the point cloud data at the second moment. The endpoint coordinates of the second line segment are compensated based on the relative pose matrix. This compensation reduces the interference of normal robot translation and small-angle movements on line segment matching, transforming the endpoint coordinates of the second line segment into the coordinate system of the point cloud data at the first moment. Subsequently, based on the endpoint coordinates of the first line segment and the compensated endpoint coordinates of the second line segment, the difference parameters between the first and second line segments are determined, which can improve the accuracy of the difference parameters to some extent.

[0112] Optionally, the step of determining the selection cost of each of the multiple candidate line segments based on the difference parameters between each of the multiple candidate line segments and the first line segment may specifically include: Step S721: For each of the plurality of candidate line segments, based on the preset direction difference weight, length difference weight, and midpoint distance weight, calculate the weighted sum of the direction difference, length difference, and midpoint distance between the candidate line segment and the first line segment to obtain the selection cost of the candidate line segment.

[0113] Among them, the smaller the selection cost, the closer the candidate line segment is to the first line segment, and the greater the probability that the two can correspond to the same environmental structure. Preset directional difference weights. Length difference weight Midpoint distance weight These can be preset, and this application embodiment does not limit this. For example, in one implementation, the direction difference weight, length difference weight, and midpoint distance weight can be respectively... , , .

[0114] The cost of selecting this candidate line segment can be defined as:

[0115] In this embodiment, based on preset direction difference weight, length difference weight, and midpoint distance weight, the weighted sum of the direction difference, length difference, and midpoint distance between the candidate line segment and the first line segment is calculated, and the selection cost of the candidate line segment can be obtained, which can ensure the efficiency of the selection cost calculation to a certain extent.

[0116] Figure 3 This is a block diagram of a map updating device provided in an embodiment of this application. The device includes: Memory 201 and processor 202; The memory 201 is used to store program code; The processor 202 calls the program code, which, when executed, performs the following operations: During the robot mapping process, if the change in the target attitude angle of the robot's environmental sensor from the first moment to the second moment exceeds a preset change threshold, the data sensed by the environmental sensor at the second moment is determined to be abnormal data. The second moment is later than the first moment. The target attitude angle includes pitch angle or roll angle. Based on the second time point and the target duration, the abnormal period is determined; The data detected by the environmental sensors during the abnormal period was determined to be abnormal data. The display device is prohibited from displaying data related to the abnormal data.

[0117] In summary, in the map updating device provided in this application embodiment, during the robot mapping process, in response to the change in the target attitude angle of the robot's environmental sensors from a first moment to a second moment exceeding a preset change threshold, the data perceived by the environmental sensors at the second moment is determined to be abnormal data. The second moment is later than the first moment, and the target attitude angle includes pitch or roll angles. The display device is prohibited from displaying data related to the abnormal data. Thus, by dynamically determining whether to classify the data perceived by the environmental sensors as abnormal data based on the change in the target attitude angle of the robot's environmental sensors at different moments during the mapping process, and by determining that the data perceived by the environmental sensors at the second moment is abnormal data when the change in the target attitude angle exceeds a preset change threshold, and prohibiting the display device from displaying data related to the abnormal data, map contamination by abnormal data can be avoided, thereby improving map quality to a certain extent.

[0118] Furthermore, in this embodiment, in response to the change in the target attitude angle of the robot's environmental sensor from the first moment to the second moment exceeding the preset change threshold, the abnormal time period is further determined, and the data detected during the abnormal time period is directly determined as abnormal data. In this way, the data collected by the robot during the continuous abnormal event can be avoided from being used for map updates, which would lead to map pollution, and thus further improve map quality.

[0119] Optionally, the processor 202 is further configured to perform: In response to the robot not being in a yaw motion state and the change in the target attitude angle exceeding the preset change threshold, the data sensed by the environmental sensor at the second moment is determined to be abnormal data.

[0120] Optionally, the processor 202 is further configured to perform: When the robot is in a yaw motion, the data sensed by the environmental sensors is determined to be normal data; Control the display device to display data related to the normal data.

[0121] Optionally, the processor 202 is further configured to perform: If the quality indicators of the abnormal data meet the preset requirements, the display device is controlled to display the robot's positioning information according to the pose information of the abnormal data.

[0122] Optionally, the processor 202 is further configured to perform: If the change in the target attitude angle of the environmental sensor from the third time to the fourth time exceeds the preset change threshold, and the fourth time is not located in the abnormal time period, then a new abnormal time period is determined for the robot based on the fourth time and the target duration.

[0123] Optionally, the processor 202 is further configured to perform: When the environmental sensor includes a lidar, based on the point cloud data collected by the lidar at a first moment and the point cloud data collected by the lidar at a second moment, it is determined that the change in the target attitude angle of the lidar from the first moment to the second moment exceeds the preset change threshold; or When the environmental sensor includes a camera, based on the image captured by the camera at a first moment and the image captured by the camera at a second moment, it is determined that the change in the target attitude angle of the camera from the first moment to the second moment exceeds a preset change threshold.

[0124] Optionally, the processor 202 is further configured to perform: Determine the difference parameter between the first line segment in the point cloud data at the first moment and the second line segment in the point cloud data at the second moment of the lidar; In response to the absence of a second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold, it is determined that the change in the target attitude angle exceeds the preset change threshold.

[0125] Optionally, the processor 202 is further configured to perform: When the robot is equipped with an inertial measurement unit, the detection data of the inertial measurement unit is acquired; The step of determining that the change in the target attitude angle exceeds the preset change threshold in response to the absence of a second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold includes: In response to the absence of a second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold, and the detection data indicating that the target posture angle of the robot is greater than a preset posture angle threshold, it is determined that the change in the target posture angle exceeds the preset change threshold.

[0126] Optionally, the processor 202 is further configured to perform: For each of the plurality of second line segments, in response to the difference parameter between the second line segment and the first line segment being less than the preset parameter threshold, the second line segment is regarded as a candidate line segment to obtain a plurality of candidate line segments; Based on the difference parameters between each of the multiple candidate line segments and the first line segment, the selection cost of each of the multiple candidate line segments is determined; If the candidate line segment with the lowest cost is not selected to form a valid line segment pair with the first line segment, then the candidate line segment with the lowest cost is selected to form a valid line segment pair with the first line segment. In response to the number of valid line segment pairs being 0, it is determined that there is no second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold.

[0127] Optionally, the processor 202 is further configured to perform: Based on the pose matrix of the point cloud data at the first time step and the pose matrix of the point cloud data at the second time step, the relative pose matrix of the point cloud data at the first time step is determined. The endpoint coordinates of the second line segment are compensated based on the relative pose matrix; Based on the endpoint coordinates of the first line segment and the compensated endpoint coordinates of the second line segment, the difference parameter between the first line segment and the second line segment is determined.

[0128] Optionally, the difference parameters include the direction difference, length difference, and midpoint distance between line segments; The processor 202 is also used to perform: For each of the plurality of candidate line segments, based on preset direction difference weight, length difference weight, and midpoint distance weight, the weighted sum of the direction difference, length difference, and midpoint distance between the candidate line segment and the first line segment is calculated to obtain the selection cost of the candidate line segment.

[0129] The processor 202 is also used to perform: The target duration is determined based on road surface condition information in the direction of travel, wherein the road surface condition information indicates at least one of the following: the degree, number, and size of depressions and / or bumps in the road surface in the direction of travel; and / or The target duration is determined based on the robot's travel speed.

[0130] This application provides an electronic device, including a processor, a memory, a communication interface, and a communication bus. The processor, the memory, and the communication interface communicate with each other via the communication bus; the memory stores at least one executable instruction, which causes the processor to execute the map update method described in the preceding embodiment.

[0131] This application provides a readable storage medium storing instructions that, when executed by one or more processors, enable the processors to perform the map update method described in the foregoing embodiments. The readable storage medium can be one or more.

[0132] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the map update method described in the foregoing embodiments.

[0133] 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.

[0134] 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.

[0135] It should be noted that all actions involving the acquisition of signals, information, or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where the application is located, and with the authorization granted by the owner of the relevant device.

[0136] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0137] This disclosure also provides an embodiment, including the following examples.

[0138] Example 1: A map update method, the method comprising: During the robot mapping process, if the change in the target attitude angle of the robot's environmental sensor from the first moment to the second moment exceeds a preset change threshold, the data sensed by the environmental sensor at the second moment is determined to be abnormal data. The second moment is later than the first moment. The target attitude angle includes pitch angle or roll angle. Based on the second time point and the target duration, the abnormal period is determined; The data detected by the environmental sensors during the abnormal period was determined to be abnormal data. The display device is prohibited from displaying data related to the abnormal data.

[0139] Example 2: According to the method described in Example 1, the step of determining that the data sensed by the environmental sensor at the second time is abnormal data in response to the change in the target attitude angle of the robot's environmental sensor from a first time moment to a second time moment exceeding a preset change threshold includes: In response to the robot not being in a yaw motion state and the change in the target attitude angle exceeding the preset change threshold, the data sensed by the environmental sensor at the second moment is determined to be abnormal data.

[0140] Example 3: According to the method described in Example 2, the method further includes: When the robot is in a yaw motion, the data sensed by the environmental sensors is determined to be normal data; Control the display device to display data related to the normal data.

[0141] Example 4: According to the method described in Example 1, the method further includes: If the quality indicators of the abnormal data meet the preset requirements, the display device is controlled to display the robot's positioning information according to the pose information of the abnormal data.

[0142] Example 5: According to the method described in Example 1, the method further includes: If the change in the target attitude angle of the environmental sensor from the third time to the fourth time exceeds the preset change threshold, and the fourth time is not located in the abnormal time period, then a new abnormal time period is determined for the robot based on the fourth time and the target duration.

[0143] Example 6. According to the method described in Example 1, the method further includes: When the environmental sensor includes a lidar, based on the point cloud data collected by the lidar at a first moment and the point cloud data collected by the lidar at a second moment, it is determined that the change in the target attitude angle of the lidar from the first moment to the second moment exceeds the preset change threshold; or When the environmental sensor includes a camera, based on the image captured by the camera at a first moment and the image captured by the camera at a second moment, it is determined that the change in the target attitude angle of the camera from the first moment to the second moment exceeds a preset change threshold.

[0144] Example 7: According to the method described in Example 6, determining that the change in the target attitude angle of the lidar from the first moment to the second moment exceeds the preset change threshold based on the point cloud data collected by the lidar at the first moment and the point cloud data collected by the lidar at the second moment includes: Determine the difference parameter between the first line segment in the point cloud data at the first moment and the second line segment in the point cloud data at the second moment of the lidar; In response to the absence of a second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold, it is determined that the change in the target attitude angle exceeds the preset change threshold.

[0145] Example 8. According to the method described in Example 7, the method further includes: When the robot is equipped with an inertial measurement unit, the detection data of the inertial measurement unit is acquired; The step of determining that the change in the target attitude angle exceeds the preset change threshold in response to the absence of a second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold includes: In response to the absence of a second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold, and the detection data indicating that the target posture angle of the robot is greater than a preset posture angle threshold, it is determined that the change in the target posture angle exceeds the preset change threshold.

[0146] Example 9: According to the method described in Example 7, the second line segment has multiple segments, and the method further includes: For each of the plurality of second line segments, in response to the difference parameter between the second line segment and the first line segment being less than the preset parameter threshold, the second line segment is regarded as a candidate line segment to obtain a plurality of candidate line segments; Based on the difference parameters between each of the multiple candidate line segments and the first line segment, the selection cost of each of the multiple candidate line segments is determined; If the candidate line segment with the lowest cost is not selected to form a valid line segment pair with the first line segment, then the candidate line segment with the lowest cost is selected to form a valid line segment pair with the first line segment. In response to the number of valid line segment pairs being 0, it is determined that there is no second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold.

[0147] Example 10: According to the method described in Example 7, determining the difference parameter between the first line segment in the point cloud data at the first moment and the second line segment in the point cloud data at the second moment of the lidar includes: Based on the pose matrix of the point cloud data at the first time step and the pose matrix of the point cloud data at the second time step, the relative pose matrix of the point cloud data at the first time step is determined. The endpoint coordinates of the second line segment are compensated based on the relative pose matrix; Based on the endpoint coordinates of the first line segment and the compensated endpoint coordinates of the second line segment, the difference parameter between the first line segment and the second line segment is determined.

[0148] Example 11: According to the method described in Example 9, the difference parameters include the direction difference, length difference, and midpoint distance between line segments; The step of determining the selection cost of each of the multiple candidate line segments based on the difference parameters between each of the multiple candidate line segments and the first line segment includes: For each of the plurality of candidate line segments, based on preset direction difference weight, length difference weight, and midpoint distance weight, the weighted sum of the direction difference, length difference, and midpoint distance between the candidate line segment and the first line segment is calculated to obtain the selection cost of the candidate line segment.

[0149] Example 12. According to any one of Examples 1-11, the method further includes: Set an anomaly flag for the abnormal data; Identify key data from data for which no abnormality marker has been set, and control the display device to display data related to the key data; Calculate the quality index of the data with the anomaly markers set, and obtain the quality index of the anomaly data.

[0150] Example 13: According to the method described in Example 2, the method further includes: When the environmental sensor includes a lidar, the relative pose matrix of the point cloud data acquired by the lidar at a first moment and the relative pose matrix of the point cloud data acquired by the lidar at a second moment are used to determine the relative rotation angle from the first moment to the second moment; if the relative rotation angle is greater than a preset rotation angle, the robot is determined to be in a yaw motion state; if the relative rotation angle is not greater than the preset rotation angle, the robot is determined not to be in a yaw motion state; or When the environmental sensor includes a camera, the relative rotation angle from the first moment to the second moment is determined based on the image captured by the camera at a first moment and the image captured by the camera at a second moment; if the relative rotation angle is greater than a preset rotation angle, the robot is determined to be in a yaw motion state; if the relative rotation angle is not greater than the preset rotation angle, the robot is determined not to be in a yaw motion state.

[0151] Example 14. According to any one of Examples 1-11, the method further includes: The target duration is determined based on road surface condition information in the direction of travel, wherein the road surface condition information indicates at least one of the following: the degree, number, and size of depressions and / or bumps in the road surface in the direction of travel; and / or The target duration is determined based on the robot's travel speed.

[0152] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors can be used in practice to implement some or all of the functions of some or all of the components in the control terminal according to the embodiments of this application. This application can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can take the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0153] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The terms "an embodiment," "embodiment," or "one or more embodiments" as used herein mean that a specific feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of this application. Furthermore, please note that the phrase "in one embodiment" does not necessarily refer to the same embodiment in all instances.

[0154] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0155] In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A map updating method, characterized in that, The method includes: During the robot mapping process, when the robot's environmental sensors include LiDAR, the relative pose matrix of the point cloud data at the first moment is determined based on the pose matrix of the point cloud data at the first moment and the pose matrix of the point cloud data at the second moment. The endpoint coordinates of the second line segment in the point cloud data at the second time moment are compensated based on the relative pose matrix. Based on the endpoint coordinates of the first line segment and the compensated endpoint coordinates of the second line segment in the point cloud data at the first moment, the difference parameter between the first line segment and the second line segment is determined. In response to the absence of a second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold, it is determined that the change in the target attitude angle of the lidar from the first time to the second time exceeds a preset change threshold. In response to the change in the target attitude angle exceeding the preset change threshold, the data sensed by the environmental sensor at the second moment is determined to be abnormal data. The second moment is later than the first moment. The target attitude angle includes pitch angle or roll angle. Based on the second time point and the target duration, the abnormal period is determined; The data detected by the environmental sensors during the abnormal period was determined to be abnormal data. The display device is prohibited from displaying data related to the abnormal data.

2. The method according to claim 1, characterized in that, The step of determining that the data sensed by the environmental sensor at the second moment is abnormal data in response to the change in the target attitude angle exceeding the preset change threshold includes: In response to the robot not being in a yaw motion state and the change in the target attitude angle exceeding the preset change threshold, the data sensed by the environmental sensor at the second moment is determined to be abnormal data.

3. The method according to claim 2, characterized in that, The method further includes: When the robot is in a yaw motion, the data sensed by the environmental sensors is determined to be normal data; Control the display device to display data related to the normal data.

4. The method according to claim 1, characterized in that, The method further includes: If the quality indicators of the abnormal data meet the preset requirements, the display device is controlled to display the robot's positioning information according to the pose information of the abnormal data.

5. The method according to claim 1, characterized in that, The method further includes: If the change in the target attitude angle of the environmental sensor from the third time to the fourth time exceeds the preset change threshold, and the fourth time is not located in the abnormal time period, then a new abnormal time period is determined for the robot based on the fourth time and the target duration.

6. The method according to claim 1, characterized in that, The method further includes: When the robot is equipped with an inertial measurement unit, the detection data of the inertial measurement unit is acquired; The step of determining that the change in the target attitude angle of the lidar from the first time moment to the second time moment exceeds a preset change threshold in response to the absence of a second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold includes: In response to the absence of a second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold, and the detection data indicating that the target posture angle of the robot is greater than a preset posture angle threshold, it is determined that the change in the target posture angle exceeds the preset change threshold.

7. The method according to claim 1, characterized in that, The second line segment has multiple segments, and the method further includes: For each of the plurality of second line segments, in response to the difference parameter between the second line segment and the first line segment being less than the preset parameter threshold, the second line segment is regarded as a candidate line segment to obtain a plurality of candidate line segments; Based on the difference parameters between each of the multiple candidate line segments and the first line segment, the selection cost of each of the multiple candidate line segments is determined; If the candidate line segment with the lowest cost is not selected to form a valid line segment pair with the first line segment, then the candidate line segment with the lowest cost is selected to form a valid line segment pair with the first line segment. In response to the number of valid line segment pairs being 0, it is determined that there is no second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold.

8. The method according to claim 7, characterized in that, The difference parameters include the direction difference, length difference, and midpoint distance between line segments; The step of determining the selection cost of each of the multiple candidate line segments based on the difference parameters between each of the multiple candidate line segments and the first line segment includes: For each of the plurality of candidate line segments, based on preset direction difference weight, length difference weight, and midpoint distance weight, the weighted sum of the direction difference, length difference, and midpoint distance between the candidate line segment and the first line segment is calculated to obtain the selection cost of the candidate line segment.

9. The method according to any one of claims 1-8, characterized in that, The method further includes: The target duration is determined based on road surface condition information in the direction of travel, wherein the road surface condition information indicates at least one of the following: the degree, number, and size of depressions and / or bumps in the road surface in the direction of travel; and / or The target duration is determined based on the robot's travel speed.

10. A map updating device, characterized in that, The device includes a memory and a processor; The memory is used to store program code; The processor calls the program code, which, when executed, performs the following operations: during the robot mapping process, when the robot's environmental sensors include LiDAR, based on the pose matrix of the point cloud data at the first moment and the pose matrix of the point cloud data at the second moment, determines the relative pose matrix of the point cloud data at the first moment. The endpoint coordinates of the second line segment in the point cloud data at the second time moment are compensated based on the relative pose matrix. Based on the endpoint coordinates of the first line segment and the compensated endpoint coordinates of the second line segment in the point cloud data at the first moment, the difference parameter between the first line segment and the second line segment is determined. In response to the absence of a second line segment whose difference parameter from the first line segment is not greater than a preset parameter threshold, it is determined that the change in the target attitude angle of the lidar from the first time to the second time exceeds a preset change threshold. In response to the change in the target attitude angle exceeding the preset change threshold, the data sensed by the environmental sensor at the second moment is determined to be abnormal data. The second moment is later than the first moment. The target attitude angle includes pitch angle or roll angle. Based on the second time point and the target duration, the abnormal period is determined; The data detected by the environmental sensors during the abnormal period was determined to be abnormal data. The display device is prohibited from displaying data related to the abnormal data.

11. A readable storage medium, characterized in that, It stores instructions that, when executed by one or more processors, cause the processors to perform the method as described in any one of claims 1 to 9.

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