Robot mapping error determination method and robot

Abstract

The invention discloses a robot mapping error determination method and a robot, and belongs to the field of robots. The method for determining the mapping error of the robot comprises the following steps: acquiring a target path of the robot in a preset indoor space; in the process that the robot moves along the target path, whether an obstacle exists in a preset range of the current advancing path of the robot or not is sensed in real time, and a target point cloud map of the preset indoor space is generated based on the sensing result; according to corresponding first distance information between the side surfaces of any two specific markers and / or between the side surface of any one specific marker and the wall surface on the target point cloud map, a mapping error is calculated; and under the condition that the mapping error is smaller than or equal to a preset threshold value, determining that the precision of the target point cloud map meets the requirement. The method for determining the mapping error of the robot has relatively high evaluation precision and accuracy, is suitable for an indoor environment, and can improve the reliability of precision test and evaluation of an indoor point cloud map.

Description

Methods for determining robot mapping errors and robots Technical Field

[0001] This application belongs to the field of robotics, and in particular relates to a method for determining robot mapping errors and a robot. Background Technology

[0002] In related technologies, the main demand for accuracy testing of 3D point cloud maps is in outdoor scenarios in the field of autonomous driving. The main methods are: one is to judge the accuracy of point cloud maps by using RTK ground truth, and the other is to obtain the position information of the same landmark in multiple point cloud maps for accuracy testing. However, the latter requires testing based on the same world coordinate system scale, the testing steps are complicated, the testing accuracy is low, and it cannot be applied to indoor mapping error measurement. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a method for determining robot mapping errors and a robot, which has high evaluation accuracy and precision, is suitable for indoor environments, and can improve the reliability of indoor point cloud map accuracy testing and evaluation.

[0004] In a first aspect, this application provides a method for determining robot mapping error, the method comprising:

[0005] Obtain the robot's target path within a pre-defined indoor space;

[0006] During the robot's movement along the target path, the system senses in real time whether there are obstacles within a preset range of the robot's current path and generates a target point cloud map of the preset indoor space based on the sensing results; wherein, the obstacles include at least two specific markers, the specific markers are cubic structures, and the sides of the specific markers are perpendicular or parallel to the walls of the preset indoor space.

[0007] The mapping error is calculated based on the first distance information on the target point cloud map between the sides of any two of the specific landmarks, and / or between the side of any one of the specific landmarks and the wall.

[0008] If the mapping error is less than or equal to a preset threshold, the accuracy of the target point cloud map is determined to meet the requirements.

[0009] According to the method for determining robot mapping error in this application, by setting multiple specific markers, the robot is controlled to map along the target path. The mapping error is evaluated based on the first distance information between the sides of any two specific markers on the constructed target point cloud map, and / or between the side of any specific marker and the wall. The method is simple and convenient to operate, has high evaluation accuracy and precision, and is suitable for indoor environments, thereby improving the reliability of indoor point cloud map accuracy testing and evaluation.

[0010] According to one embodiment of this application, before sensing in real time whether there are obstacles within a preset range of the robot's current travel path during the robot's movement along the target path, the method further includes:

[0011] When the robot moves to the starting point of the target path, and a laser device perpendicularly set on the side of the wall or a specific landmark emits a laser signal toward the reflecting device, the robot's pose is adjusted so that the angle between the incident light path and the reflected light path of the laser signal does not exceed the target threshold, and the robot's pose in the current state is determined as the acquisition pose.

[0012] The reflective device is mounted on the robot and is at the same height as the robot.

[0013] According to one embodiment of this application, the step of calculating the mapping error based on the first distance information corresponding to the sides of any two specific landmarks and / or the side of any one specific landmark and the wall on the target point cloud map includes:

[0014] Based on the first distance information on the target point cloud map between any two sides of the specific landmark and / or between any side of the specific landmark and the wall, calculate at least one target error corresponding to any side.

[0015] The mapping error is calculated based on the target error corresponding to each of the aforementioned sides.

[0016] According to one embodiment of this application, the step of calculating at least one target error corresponding to any one of the sides based on the first distance information corresponding to the sides of any two of the specific landmarks on the target point cloud map, and / or the side of any one of the specific landmarks and the wall, includes:

[0017] The target error is calculated based on the difference between the second distance information detected between the sides of any two of the specific markers and between any one of the specific markers and the wall, and the first distance information.

[0018] According to one embodiment of this application, the step of calculating at least one target error corresponding to any one of the sides based on the first distance information corresponding to the sides of any two of the specific landmarks on the target point cloud map, and / or the side of any one of the specific landmarks and the wall, includes:

[0019] When there are multiple target point cloud maps, the target error is calculated based on the difference between the corresponding first distance information on any two target point cloud maps, based on the difference between the sides of the same two specific landmarks and / or between the sides of the same specific landmarks and the wall.

[0020] According to one embodiment of this application, calculating the mapping error based on the target error corresponding to each of the said sides includes:

[0021] The maximum value among at least one target error corresponding to the side is determined as the first error corresponding to the side.

[0022] The mapping error is calculated based on the average value of the first error corresponding to each of the aforementioned sides.

[0023] According to one embodiment of this application, the step of calculating the mapping error based on the first distance information corresponding to the sides of any two specific landmarks and / or the side of any one specific landmark and the wall on the target point cloud map includes:

[0024] When there are multiple target point cloud maps, the mapping error is updated based on the average value of the mapping error calculated from each target point cloud map.

[0025] According to one embodiment of this application, the first distance information is determined according to the following steps:

[0026] Perform at least one filtering process and height truncation process on the coordinate data in the target point cloud map;

[0027] The remaining coordinate data are fitted to the side surface to obtain the centroid coordinates of each side surface and the corresponding side surface of the wall.

[0028] The first distance information is calculated based on the side centroid coordinates corresponding to the sides of any two of the specific landmarks, and / or based on the side centroid coordinates corresponding to the side of any one of the specific landmarks and the wall.

[0029] Secondly, this application provides a device for determining robot mapping errors, applied to a preset indoor space, wherein multiple specific markers are placed in the preset indoor space, the sides of the multiple specific markers are parallel or perpendicular to each other, and the sides of each specific marker are parallel or perpendicular to the wall of the preset indoor space; the device includes:

[0030] The first processing module is used to obtain the target path of the robot in the preset indoor space;

[0031] The second processing module is used to sense in real time whether there are obstacles within a preset range of the robot's current travel path during the robot's movement along the target path, and generate a target point cloud map of the preset indoor space based on the sensing results; wherein, the obstacles include at least two specific markers, the specific markers are cubic structures, and the sides of the specific markers are perpendicular or parallel to the walls of the preset indoor space.

[0032] The third processing module is used to calculate the mapping error based on the first distance information corresponding to the sides of any two of the specific landmarks and / or the side of any one of the specific landmarks and the wall on the target point cloud map.

[0033] The fourth processing module is used to determine whether the accuracy of the target point cloud map meets the requirements when the mapping error is less than or equal to a preset threshold.

[0034] According to the robot mapping error determination device of this application, by setting multiple specific markers, the robot is controlled to map along the target path. The mapping error is evaluated based on the first distance information between the sides of any two specific markers on the constructed target point cloud map, and / or between the side of any specific marker and the wall. The device is simple and convenient to operate, has high evaluation accuracy and precision, and is suitable for indoor environments, which can improve the reliability of indoor point cloud map accuracy testing and evaluation.

[0035] Thirdly, this application provides a robot that performs accuracy measurements based on the robot mapping error determination method described in the first aspect above.

[0036] Fourthly, this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for determining robot mapping error as described in the first aspect above.

[0037] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method for determining robot mapping error as described in the first aspect above. Attached Figure Description

[0038] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0039] Figure 1 is a flowchart illustrating one of the methods for determining robot mapping errors provided in an embodiment of this application;

[0040] Figure 2 is a schematic diagram of the specific marker setting method of the robot mapping error determination method provided in the embodiments of this application;

[0041] Figure 3 is a second schematic flowchart of the method for determining robot mapping error provided in the embodiments of this application;

[0042] Figure 4 is a flowchart of the method for determining robot mapping error provided in the embodiments of this application (Part 3).

[0043] Figure 5 is one of the schematic diagrams showing the results of the method for determining robot mapping error provided in the embodiments of this application;

[0044] Figure 6 is a second schematic diagram showing the results of the method for determining robot mapping error provided in the embodiments of this application;

[0045] Figure 7 is a flowchart of the method for determining robot mapping error provided in the embodiments of this application (fourth one);

[0046] Figure 8 is a flowchart of the method for determining robot mapping error provided in the embodiments of this application (the fifth one).

[0047] Figure 9 is a schematic diagram of the target point determination method of the robot mapping error determination method provided in the embodiment of this application;

[0048] Figure 10 is a schematic diagram of the structure of the robot mapping error determination device provided in the embodiment of this application;

[0049] Figure 11 is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. Detailed Implementation

[0050] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, 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 are within the scope of protection of this application.

[0051] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0052] The following description, in conjunction with the accompanying drawings, details the method for determining robot mapping errors, the device for determining robot mapping errors, the robot, and the readable storage medium provided in this application, through specific embodiments and application scenarios.

[0053] The method for determining robot mapping error can be applied to the terminal, specifically executed by the hardware or software within the terminal.

[0054] The robot mapping error determination method provided in this application embodiment can be executed by a robot, or by a functional module or entity in the robot that can implement the robot mapping error determination method, or by a server that communicates with the robot. The robot mapping error determination method provided in this application embodiment will be described below with the robot as the execution subject as an example.

[0055] The inventors discovered that the factors affecting the mapping accuracy of indoor 3D point cloud maps mainly include the following aspects: 1) whether the installation position of the robot sensors is reasonable; 2) whether the mapping algorithm of Simultaneous Localization and Mapping (SLAM) is correct; 3) whether the user's operation is correct during the actual mapping process, etc. It can be seen that the factors affecting the accuracy of indoor 3D point cloud maps exist throughout the entire life cycle of the robot, including but not limited to the robot's manufacturing and actual application scenarios. Therefore, it is particularly important to quickly and accurately determine the mapping accuracy of indoor point cloud maps to assist manufacturers and users in rapid maintenance.

[0056] Currently, the main demand for accuracy testing of 3D point cloud maps is in outdoor scenarios in the field of autonomous driving. The main methods are twofold: first, to judge the accuracy of point cloud maps through RTK ground truth; and second, to obtain the position information of the same landmarks in multiple point cloud maps for accuracy testing. However, both methods require testing based on the same world coordinate system. For indoor 3D point cloud map construction algorithms, the lack of RTK ground truth information for reference results in a lack of an easy-to-implement and accurate method for measuring the accuracy of indoor 3D point cloud maps.

[0057] Based on the above considerations, in order to solve the problem of inapplicability to indoor mapping error measurement, the inventors, after in-depth research, designed a method for determining robot mapping error, including: acquiring the target path of the robot in a preset indoor space; during the robot's movement along the target path, sensing in real time whether there are obstacles within a preset range of the robot's current path, and generating a target point cloud map of the preset indoor space based on the sensing results; wherein, the obstacles include at least two specific markers, the specific markers are cubic structures, and the sides of the specific markers are perpendicular or parallel to the walls of the preset indoor space; calculating the mapping error based on the first distance information corresponding to the sides of any two specific markers and / or the side of any one specific marker and the wall on the target point cloud map; and determining that the accuracy of the target point cloud map meets the requirements if the mapping error is less than or equal to a preset threshold.

[0058] In this method for determining robot mapping error, multiple specific markers are set, and the robot is controlled to map along the target path. The mapping error is evaluated based on the first distance information between the sides of any two specific markers on the constructed target point cloud map, and / or between the side of any specific marker and the wall. The method is simple and convenient to operate, has high evaluation accuracy and precision, and is suitable for indoor environments, which can improve the reliability of indoor point cloud map accuracy testing and evaluation.

[0059] As shown in Figure 1, the method for determining the mapping error of the robot includes steps 110, 120, 130 and 140.

[0060] Step 110: Obtain the target path of the robot in the preset indoor space;

[0061] In this step, the pre-defined indoor space can be an open space without obvious obstacles.

[0062] In some embodiments, the preset indoor space can be the space corresponding to the scene to be mapped, such as selecting the living room, and setting multiple specific landmarks in the living room area.

[0063] Before measurement, multiple specific markers can be placed in a pre-set indoor space. The sides of the multiple specific markers are parallel or perpendicular to each other, and the sides of each specific marker are parallel or perpendicular to the walls of the pre-set indoor space, as shown in Figure 2.

[0064] Among them, specific markers can be objects with planar structures, such as cubic structures.

[0065] The number of specific markers can be 5, 6, or other values.

[0066] In some embodiments, the number of specific markers may be no less than 10.

[0067] The target path is the movement path of the robot during the mapping process.

[0068] The target path can be set based on the placement of specific landmarks in the preset indoor space, such as user-defined paths, to ensure that the robot can scan the entire preset indoor space, or scan the sides of all specific landmarks and the walls of the preset indoor space as it moves along the target path.

[0069] It should be noted that there should be at least five specific markers on the target path to reduce the impact of some distortions caused during the mapping process.

[0070] In some embodiments, a specific marker can be placed at each of the four corners and the center of the preset indoor space. Placing specific markers at the four corners ensures that the robot can scan the entire range of the preset indoor space. Placing specific markers at the center reduces the probability of local deformation and allows for the measurement of the mapping accuracy of the entire preset indoor space as well as the mapping accuracy of local areas.

[0071] It is understandable that when multiple mapping operations are performed, the target path corresponding to each mapping operation can be the same path or a different path.

[0072] Step 120: During the robot's movement along the target path, sense in real time whether there are obstacles within the preset range of the robot's current path, and generate a target point cloud map of the preset indoor space based on the sensing results.

[0073] In this step, the preset range can be user-defined, such as setting it to an area centered on the robot with a radius of a certain small length.

[0074] The obstacle should include at least two specific markers.

[0075] Obstacles may also include: walls in the preset interior space and other objects in the preset interior space, such as tables, chairs, sofas, etc.

[0076] During the mapping process, obstacles can be detected by sensors installed on the robot, such as LiDAR, and a target point cloud map can be constructed based on the sensing results when obstacles are detected.

[0077] The sensing results include information such as the distance between obstacles and the robot.

[0078] During a mapping process, the robot can sequentially collect multiple frames of point cloud maps, overlay the point cloud map of the current frame with the point cloud map of the previous frame, and repeat this process until the last frame of point cloud map is collected, thereby constructing the target point cloud map.

[0079] Step 130: Calculate the mapping error based on the first distance information on the target point cloud map corresponding to the sides of any two specific landmarks and / or the side of any specific landmark and the wall.

[0080] In this step, the first distance information is used to characterize the shortest distance on the target point cloud map between the sides of any two specific landmarks, or between the side of any specific landmark and the wall.

[0081] As shown in Figure 2, the first distance information can be the distance between the right side of a specific marker on the left side of the last row of the target point cloud map and the left side of a specific marker on the right side of the last row; or it can be the distance between the right side of a specific marker on the left side of the last row of the target point cloud map and the right wall.

[0082] Mapping error is used to characterize the accuracy of a robot's mapping and to evaluate the quality of the robot's mapping. The smaller the mapping error, the higher the accuracy and the better the mapping quality.

[0083] After calculating the first distance information between each side and / or the first distance information between the side and the wall, the mapping error can be evaluated based on the calculated first distance information.

[0084] In some embodiments, mapping error may include absolute accuracy or relative accuracy.

[0085] Different types of mapping errors have different calculation methods.

[0086] Absolute accuracy can be determined by the first distance information calculated based on the current target point cloud map and the actual distance, while relative accuracy can be determined by the distance measured or calculated in other cases based on the first distance information calculated based on the current target point cloud map.

[0087] In actual implementation, the corresponding category of mapping error can be selected based on user needs or actual conditions.

[0088] The calculation method for the first distance information is explained below.

[0089] In some embodiments, the first distance information can be determined by the following steps:

[0090] Perform at least one filtering process and height truncation process on each coordinate data in the target point cloud map;

[0091] The remaining coordinate data are fitted to the side to obtain the side centroid coordinates of each side and the wall.

[0092] The first distance information is calculated based on the side centroid coordinates corresponding to the sides of any two specific landmarks, and / or based on the side centroid coordinates corresponding to the sides of any one specific landmark and the wall.

[0093] In this embodiment, filtering is used to remove outliers, and height truncation is used to filter out noisy data such as flat ceilings.

[0094] Filtering processes can include, but are not limited to: radius filtering, probability filtering, and statistical filtering.

[0095] In actual implementation, algorithms such as Random Sample Consensus (RANSAC) and Iterative Closest Point (ICP) can be used to detect and fit each plane from the remaining coordinate data, obtain the surfaces of each side and wall, and obtain the coordinate data corresponding to each surface, so as to use the plane to replace the single point to measure the distance.

[0096] Coordinate data can be collected by devices such as lidar on the robot.

[0097] Coordinate data can include (x, y, z) coordinates.

[0098] It should be noted that when the robot is calibrated to ensure that it is parallel or perpendicular to each side, the vertical coordinate (z-coordinate) of each coordinate data point can be approximated as the same, and the coordinate data can be further optimized to (x, y) coordinates.

[0099] Obtain the centroid coordinates (x, y) of each side and wall surface, and calculate the first distance information between each surface based on the centroid coordinates of each surface.

[0100] In some embodiments, multiple side fittings can be performed to obtain multiple centroid coordinates corresponding to each side. Based on each centroid coordinate, multiple first distance information between the sides of any two specific landmarks and multiple first distance information between any specific landmark and the wall can be obtained. The average value of the multiple first distance information is taken as the first distance information between the two surfaces.

[0101] According to the robot mapping error determination method provided in the embodiments of this application, by using a plane instead of a single point to measure distance, some measurement errors can be eliminated, and the measurement accuracy and precision can be improved.

[0102] Step 140: If the mapping error is less than or equal to the preset threshold, determine that the accuracy of the target point cloud map meets the requirements.

[0103] In this step, the preset threshold can be user-defined; the higher the requirement for map quality, the smaller the preset threshold can be.

[0104] If the mapping error is less than or equal to the preset threshold, it indicates that the mapping error is within a reasonable range or approximately negligible. The accuracy of the target point cloud map is considered to meet the requirements, and the currently constructed target point cloud map can be saved.

[0105] As shown in Figure 7, the constructed target point cloud map can be saved in the robot's memory, or it can be uploaded to the cloud for storage as needed.

[0106] As shown in Figure 5, the constructed target point cloud map can be displayed in the corresponding display area of ​​the display device, and controls are provided for users to input, such as starting map building, ending map building, and whether to upload and obtain map building errors.

[0107] For example, in a user scenario, when a user uses a robot to build a map, they can interact with the robot through the page shown in Figure 5, build the scene shown in Figure 2, and ensure that the robot is perpendicular to one side of the wall. The user can then use the "Start Mapping" control shown in Figure 5 to build the map.

[0108] Figure 6 illustrates the display interface corresponding to multiple map constructions.

[0109] For example, in a user scenario, when a user uses a robot to create a map, they can create multiple maps in the same preset indoor space. As shown in Figure 6, they can then select the corresponding target point cloud maps using the "Upload and Get Mapping Error" control and upload these target point cloud maps to the cloud.

[0110] In this application, by setting multiple specific markers, the robot is controlled to build a map along the target path. The mapping error is evaluated based on the first distance information between the sides of any two specific markers on the constructed target point cloud map, and / or between the side of any specific marker and the wall. This method has high evaluation accuracy and is suitable for indoor environments, thus having a wide range of application scenarios.

[0111] According to the robot mapping error determination method provided in the embodiments of this application, by setting multiple specific markers, the robot is controlled to map along the target path. The mapping error is evaluated based on the first distance information between the sides of any two specific markers on the constructed target point cloud map, and / or between the side of any specific marker and the wall. The operation is simple and convenient, with high evaluation accuracy and precision, and it is suitable for indoor environments, which can improve the reliability of indoor point cloud map accuracy testing and evaluation.

[0112] In some embodiments, after step 140, the method may further include: saving the constructed map if the test is passed based on the mapping error.

[0113] In this embodiment, as shown in Figure 7, the constructed target point cloud map can be saved in the robot's memory, or it can be uploaded to the cloud for storage as needed.

[0114] As shown in Figure 5, the constructed target point cloud map can be displayed in the corresponding display area of ​​the display device, and controls are provided for users to input, such as starting map building, ending map building, and whether to upload and obtain map building errors.

[0115] The specific calculation method for mapping error is explained below.

[0116] In some embodiments, step 130 may include:

[0117] Based on the first distance information on the target point cloud map between the sides of any two specific landmarks, and / or between the side of any specific landmark and the wall, calculate at least one target error corresponding to any side.

[0118] The mapping error is calculated based on the target error corresponding to each side.

[0119] In this embodiment, the target error is used to characterize the error of the first distance information on the target point cloud map based on the sides of any two specific landmarks and / or the sides of any one specific landmark and the wall.

[0120] The target error can be either absolute error or relative error.

[0121] By providing a variety of different error calculation methods, users can choose the appropriate calculation method based on different application scenarios or actual needs, which has high flexibility and universality.

[0122] The side of a specific landmark can be used to calculate the first distance information from the sides of any one or more other specific landmarks and the wall. Each first distance information can correspond to a target error, that is, the side of a specific landmark can correspond to multiple target errors.

[0123] In actual execution, multiple target errors corresponding to the same side can be processed, such as calculating the average value, so that the average value is determined as the final target error corresponding to that side, and the mapping error can be calculated based on the final target error corresponding to each side.

[0124] In some embodiments, calculating the mapping error based on the target error corresponding to each side may include:

[0125] The maximum value among at least one target error corresponding to the side is determined as the first error corresponding to the side.

[0126] The mapping error is calculated based on the average value of the first error corresponding to each side.

[0127] In this embodiment, the maximum value among multiple target errors corresponding to the same side can be taken as the final target error corresponding to that side, i.e., the first error.

[0128] For each side, its corresponding first error can be calculated, and then the average of the first errors corresponding to each side is taken as the mapping error.

[0129] In some embodiments, the mapping error can be calculated using the following formula:

[0130]

[0131] in, E represents mapping error. imax is the maximum value from the i-th side to other planes (including the sides and walls of other specific landmarks); k is the total number of specific landmarks, and k is a positive integer.

[0132] Of course, in other embodiments, the maximum value of the first error corresponding to each side can also be used as the mapping error, and this application does not limit this.

[0133] According to the robot mapping error determination method provided in the embodiments of this application, the error caused by point selection is reduced by averaging the difference in the first distance information between multiple faces, which can eliminate some measurement errors and improve measurement accuracy and precision.

[0134] The calculation methods for absolute precision and relative precision are explained below.

[0135] Firstly, absolute precision

[0136] As shown in Figure 3, in some embodiments, calculating at least one target error corresponding to any side based on the first distance information on the target point cloud map between the sides of any two specific landmarks, and / or between the side of any specific landmark and the wall, may include:

[0137] The target error is calculated based on the difference between the second distance information detected between the sides of any two specific markers and between any specific marker and the wall, and the first distance information.

[0138] In this embodiment, the second distance information is the actual distance between the sides of any two specific landmarks, or between any specific landmark and the wall.

[0139] In actual implementation, the second distance information can be obtained through a distance acquisition device, such as using a laser rangefinder to measure the distance between the sides of each specific marker and the distance between the specific marker and the wall, to obtain LRij (i, j = 1, 2, 3…k); where LRij is the second distance information between the i-th side and the j-th side; and k is the total number of specific markers.

[0140] It is understandable that, for side A and side B, one or more first distance information can be obtained through one or more calculations.

[0141] Taking multiple first distance information as an example, one of the first distance information can be selected as the final first distance information between side A and side B; or the multiple first distance information can be processed, such as determining the average value of the multiple first distance information as the final first distance information between side A and side B.

[0142] The initial distance information between the side and the wall is calculated in a similar way to the above method.

[0143] After obtaining the first distance information between side A and side B, the absolute error between side A and side B can be calculated by combining the difference between the second distance information between side A and side B.

[0144] For example, in the process of calculating the target error, the coordinate data corresponding to side A and side B in the target point cloud map can be fitted three or more times to obtain multiple first distance information between side A and side B. Then, the average value of multiple first distance information is taken as the final first distance information between side A and side B. Using the same method, the final first distance information between any two sides and between any side and the wall can be calculated to obtain LMij (i, j = 1, 2, 3…k), where LMij is the first distance information between the i-th side and the j-th side; k is the total number of specific markers.

[0145] In some embodiments, the target error can be calculated using the following formula:

[0146] Eij=|LRij-LMij|(i, j=1, 2, 3...k)

[0147] Where Eij is the target error between the i-th face and the j-th face; LRij is the second distance information between the i-th face and the j-th face; LMij is the first distance information between the i-th face and the j-th face; and k is the total number of specific markers.

[0148] LMij is a measurable distance value in either the vertical or horizontal direction. If neither the horizontal nor vertical distance is measurable, Eij can be written as 0.

[0149] For any side, the target error between it and other sides or walls can be calculated using the method described above.

[0150] According to the robot mapping error determination method provided in the embodiments of this application, the target error is calculated by using the first distance information between the first target side and the second target side obtained from the point cloud map obtained by robot mapping, and the second distance information between the first target side and the second target side, so as to evaluate the mapping error based on the target error, which has high evaluation accuracy.

[0151] Secondly, relative accuracy

[0152] In some embodiments, calculating at least one target error corresponding to any side based on the first distance information on the target point cloud map between the sides of any two specific landmarks, and / or between the side of any specific landmark and the wall, may include:

[0153] When there are multiple target point cloud maps, the target error is calculated based on the difference between the corresponding first distance information on any two target point cloud maps, based on the difference between the sides of the same two specific landmarks and / or between the sides and walls of the same specific landmarks.

[0154] In this embodiment, the degree of difference can be expressed as a difference or a ratio, etc., and this application does not limit it.

[0155] For the same preset indoor space, the robot can be controlled to perform multiple mappings along the target path. The coordinate data collected in each mapping is processed to obtain the first distance information between any two sides and between a side and the wall for each mapping iteration.

[0156] The target error can be the difference between the first distance information determined in any two mapping processes, or it can be determined by processing the differences between multiple first distance information, such as taking the average value.

[0157] Taking the first distance information between side A and side B as an example, the first distance information between side A and side B under each target point cloud map obtained through multiple mappings can be obtained in the above manner. Then, based on the difference between the first distance information under any two target point cloud maps, the difference between side A and side B under the two target point cloud maps can be calculated, thereby obtaining multiple differences between side A and side B.

[0158] In some embodiments, the target error can be calculated using the following formula:

[0159] Eijpq=|LMijp-LMijq|(i, j=1, 2, 3...k)

[0160] Where Eijpq is the target error between the i-th and j-th faces in the p-th and q-th maps; LMijp is the first distance information between the i-th and j-th faces in the p-th map; LMijq is the first distance information between the i-th and j-th faces in the q-th map; p and q are both positive integers; k is the total number of specific markers.

[0161] In a similar manner, the target error between two identical sides and between the same side and the wall can be calculated under any two target point cloud maps. After obtaining all target errors, the maximum value among the multiple target errors can be taken as the final mapping error.

[0162] In some embodiments, multiple differences can be preprocessed to remove values ​​with differences greater than an error threshold, and the relative mapping error can be determined based on the remaining differences.

[0163] In this embodiment, the error threshold can be user-defined, such as set to 3cm, 5cm, 5.5cm or other values.

[0164] In some embodiments, the average of multiple differences can be determined as the mapping error.

[0165] For example, control the robot to build maps multiple times to obtain map 1, map 2, map 3... map n, where n is a positive integer; based on the target error 1 between map 1 and map 2, the target error 2 between map 1 and map 3, and the target error 3 between map 2 and map 3, the average of target error 1, target error 2, and target error 3 is taken as the map building error.

[0166] The method for determining robot mapping error provided in the embodiments of this application involves performing multiple mapping operations, calculating the relative error based on the first distance information under any two mapping operations, and evaluating the mapping error based on the relative error. This method is suitable for application scenarios where it is difficult to collect the second distance information. It is simple and convenient to operate, has a wide range of applicable scenarios, and has high evaluation accuracy.

[0167] In actual implementation, the corresponding category of mapping error can be selected based on user needs or actual conditions.

[0168] For example, in scenarios where the second distance information is easily obtained, such as in a factory production quality inspection scenario, a scene and specific markers as shown in Figure 2 can be set up in the factory environment. After the robot is installed, the mapping function is started at the entrance to perform a full-field scan according to the target path. When the robot retreats to the exit, the mapping ends, covering the entire field to collect coordinate data. Based on the coordinate data, a target point cloud map is constructed and uploaded to the cloud. The cloud then calculates the first distance information based on the target point cloud map and calculates the absolute accuracy of the map based on the first and second distance information, as shown in Figure 8.

[0169] For example, in a user scenario, a scene and specific landmarks as shown in Figure 2 can be built in the user scenario, and multiple maps can be built in a similar way to construct multiple target point cloud maps. Based on the multiple target point cloud maps, the first distance information corresponding to multiple different target point cloud maps can be calculated, and the relative accuracy of the map can be calculated based on the first distance information.

[0170] Referring again to Figure 3, in some embodiments, step 130 may include:

[0171] When there are multiple target point cloud maps, the mapping error is updated based on the average value of the mapping error calculated from each target point cloud map.

[0172] In this embodiment, multiple mappings can be performed on the same preset indoor space, such as controlling the robot to move along the same or different target paths to collect data multiple times. During each mapping process, it should be ensured that the robot can cover each specific landmark and wall surface of the preset indoor space.

[0173] For each map construction, the mapping error can be calculated based on steps 110 to 130 above, and then the average value of multiple mapping errors is determined as the final mapping error.

[0174] For example, mapping errors can be calculated based on the following formula:

[0175]

[0176] Where E represents the final mapping error; is the mapping error calculated for the i-th mapping; N is the number of mapping iterations (i.e., the number of target point cloud maps), where N is a positive integer.

[0177] Of course, in other embodiments, the maximum value among the multiple mapping errors determined by multiple mapping operations can be selected as the final mapping error. The specific choice can be made flexibly based on actual needs, and this application does not limit it here.

[0178] According to the robot mapping error determination method provided in the embodiments of this application, by performing multiple mapping operations, the average value of the mapping error calculated based on the point cloud map constructed each time is determined as the final mapping error, which can further improve the accuracy and precision of mapping error assessment.

[0179] As shown in Figure 4, in some embodiments, after step 130, the method may further include:

[0180] If the mapping error exceeds a preset threshold, a prompt message will be output.

[0181] In this embodiment, if the mapping error is greater than a preset threshold, it is determined that the accuracy of the target point cloud map does not meet the requirements.

[0182] The prompt message is used to remind the user to rebuild the map again, so that the target point cloud map can be uploaded for map building error calculation.

[0183] The preset threshold can be user-defined, such as 3cm, 5cm, 5.5cm or other values.

[0184] Continuing with the example of relative error, after controlling the robot to perform multiple mapping operations to obtain map 1, map 2, map 3... map n, where n is a positive integer; based on the fact that target error 1 can be calculated between map 1 and map 2, target error 2 can be calculated between map 1 and map 3, and target error 3 can be calculated between map 2 and map 3, if target error 1, target error 2, and target error 3 are all not greater than a preset threshold, then the test is considered to have passed, and the average of target error 1, target error 2, and target error 3 can be used as the mapping error of the map and saved.

[0185] In some embodiments, outputting prompt information may include: displaying prompt information through an image, broadcasting prompt information through voice, or outputting prompt information through an indicator light.

[0186] For example, referring to Figure 4, indicator lights can be set on the robot. If the test is passed, the indicator light will be green; if the test fails, the indicator light will be red to prompt the user to recalibrate the position of sensors such as the LiDAR.

[0187] According to the robot mapping error determination method provided in the embodiments of this application, after calculating the mapping error, the mapping error is further evaluated so as to output prompt information to re-build the map if the test is determined to fail. This can improve the mapping quality and thus improve the user experience.

[0188] In some embodiments, prior to step 110, the method may further include:

[0189] When the robot moves to the starting point of the target path, and a laser device that is vertically set on the side of the wall or a specific landmark emits a laser signal toward the reflecting device, the robot's pose is adjusted so that the angle between the incident light path and the reflected light path of the laser signal does not exceed the target threshold, and the robot's pose in the current state is determined as the acquisition pose.

[0190] The reflective device is mounted on the robot and is at the same height as the robot.

[0191] In this embodiment, the target threshold is a small angle value, which can be user-defined, such as 0 or 0.1°.

[0192] If the angle between the incident light path and the reflected light path does not exceed the target threshold, it can be approximately assumed that the incident light path and the reflected light path coincide.

[0193] A reflecting device is a device used to reflect light, such as a plane mirror.

[0194] As shown in Figure 9, after placing a specific marker in a preset indoor space, a laser pointer can be vertically fixed to the first specific marker on the wall or the target path, and a plane mirror can be fixed at the same height as the robot. Before mapping, move the robot to the starting point of the target path or before the first specific marker, and adjust the robot's orientation until the incident light path and the reflected light path are basically coincident, that is, the crosshair on the side of the wall or the first specific marker and the laser pointer are on the same vertical line. Then, the robot's pose at that point is determined as the acquisition pose.

[0195] By using the above initial positioning method, it can be ensured that the x-axis (the robot's front facing) is perpendicular to the wall when the robot is collecting data, which reduces human error when measuring distance and simplifies the calculation of mapping error. In addition, by assuming that the robot is perpendicular to the wall, it can be approximately assumed that all planes and the robot are at the same height, without having to consider issues such as height slope. When calculating the first distance information between each plane in the subsequent calculation, it is only necessary to use the difference in the x-direction or y-direction, which significantly simplifies the calculation steps and improves the calculation efficiency.

[0196] During the research and development process, the inventors discovered that in related technologies, 3D point cloud maps are affected by the error in the selection of reference points during measurement. This error is usually at the centimeter level, and the accuracy of 3D point cloud maps is also at the centimeter level. If the error in the selection of reference points cannot be eliminated, the accuracy of the map cannot be well reflected.

[0197] According to the robot mapping error determination method provided in the embodiments of this application, by setting specific markers in the scene and combining them with the inherent marker information such as walls that exist in the indoor space itself, the specific markers should be parallel or perpendicular to the walls. In this scene, data collection is carried out while ensuring that the mapping direction is perpendicular or parallel to the wall. This can improve the accuracy and precision of the selection of 3D point cloud reference points, avoid the influence of Z value, simplify the calculation complexity, and make the real scene and different planes in the constructed 3D point cloud map measurable and easy to compare, thereby improving the accuracy of mapping error assessment and improving the reliability of indoor point cloud map accuracy test evaluation.

[0198] The robot mapping error determination method provided in this application can be executed by a robot mapping error determination device. This application uses the robot mapping error determination device executing the robot mapping error determination method as an example to illustrate the robot mapping error determination device provided in this application.

[0199] This application also provides a device for determining robot mapping errors.

[0200] The device for determining the mapping error of the robot is applied to a preset indoor space, in which multiple specific markers are placed. The sides of the multiple specific markers are parallel or perpendicular to each other, and the sides of each specific marker are parallel or perpendicular to the walls of the preset indoor space.

[0201] As shown in Figure 10, the device for determining the mapping error of the robot includes: a first processing module 1010, a second processing module 1020, a third processing module 1030, and a fourth processing module 1040.

[0202] The first processing module 1010 is used to obtain the target path of the robot in the preset indoor space;

[0203] The second processing module 1020 is used to sense in real time whether there are obstacles within a preset range of the robot's current travel path during the robot's movement along the target path, and generate a target point cloud map of the preset indoor space based on the sensing results; wherein, the obstacles include at least two specific markers, the specific markers are cubic structures, and the sides of the specific markers are perpendicular or parallel to the walls of the preset indoor space.

[0204] The third processing module 1030 is used to calculate the mapping error based on the first distance information on the target point cloud map corresponding to the sides of any two specific landmarks and / or the side of any specific landmark and the wall.

[0205] The fourth processing module 1040 is used to determine whether the accuracy of the target point cloud map meets the requirements when the mapping error is less than or equal to a preset threshold.

[0206] The robot mapping error determination device provided in the embodiments of this application sets multiple specific markers and controls the robot to map along the target path. The mapping error is evaluated based on the first distance information between the sides of any two specific markers on the constructed target point cloud map, and / or between the side of any specific marker and the wall. The device is simple and convenient to operate, has high evaluation accuracy and precision, and is suitable for indoor environments, thereby improving the reliability of indoor point cloud map accuracy testing and evaluation.

[0207] In some embodiments, the device may further include a fifth processing module for:

[0208] Before the robot moves along the target path and before it senses in real time whether there are obstacles within the preset range of the robot's current path, when the robot moves to the starting point of the target path and the laser device, which is vertically set on the side of the wall or a specific marker, emits a laser signal towards the reflecting device, the robot's pose is adjusted so that the angle between the incident light path and the reflected light path of the laser signal does not exceed the target threshold, and the robot's pose in the current state is determined as the acquisition pose.

[0209] The reflective device is mounted on the robot and is at the same height as the robot.

[0210] In some embodiments, the third processing module 1030 can also be used for:

[0211] Based on the first distance information on the target point cloud map between the sides of any two specific landmarks, and / or between the side of any specific landmark and the wall, calculate at least one target error corresponding to any side.

[0212] The mapping error is calculated based on the target error corresponding to each side.

[0213] In some embodiments, the third processing module 1030 can also be used for:

[0214] The target error is calculated based on the difference between the second distance information detected between the sides of any two specific markers and between any specific marker and the wall, and the first distance information.

[0215] In some embodiments, the third processing module 1030 can also be used for:

[0216] When there are multiple target point cloud maps, the target error is calculated based on the difference between the corresponding first distance information on any two target point cloud maps, based on the difference between the sides of the same two specific landmarks and / or between the sides and walls of the same specific landmarks.

[0217] In some embodiments, the third processing module 1030 can also be used for:

[0218] The maximum value among at least one target error corresponding to the side is determined as the first error corresponding to the side.

[0219] The mapping error is calculated based on the average value of the first error corresponding to each side.

[0220] In some embodiments, the third processing module 1030 can also be used for:

[0221] When there are multiple target point cloud maps, the mapping error is updated based on the average value of the mapping error calculated from each target point cloud map.

[0222] In some embodiments, the device may further include a sixth processing module for:

[0223] Perform at least one filtering process and height truncation process on the coordinate data in the target point cloud map;

[0224] The remaining coordinate data are fitted to the side to obtain the side centroid coordinates of each side and the wall.

[0225] The first distance information is calculated based on the side centroid coordinates corresponding to the sides of any two specific landmarks, and / or based on the side centroid coordinates corresponding to the sides of any one specific landmark and the wall.

[0226] The device for determining robot mapping errors in this application embodiment can be an electronic device or a component within an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc. This application embodiment does not specifically limit the scope of the device.

[0227] The device for determining robot mapping errors in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit it.

[0228] The robot mapping error determination device provided in this application embodiment can realize the various processes implemented in the method embodiments of Figures 1 to 9. To avoid repetition, it will not be described again here.

[0229] This application also provides a robot.

[0230] The robot performs accuracy measurement based on the robot mapping error determination method described in any of the above embodiments.

[0231] The robot provided in the embodiments of this application sets multiple specific markers and controls the robot to build a map along the target path. The mapping error is evaluated based on the first distance information between the sides of any two specific markers on the constructed target point cloud map, and / or between the side of any specific marker and the wall. The operation is simple and convenient, with high evaluation accuracy and precision. It is also suitable for indoor environments and can improve the reliability of indoor point cloud map accuracy testing and evaluation.

[0232] In some embodiments, as shown in FIG11, this application embodiment also provides an electronic device 1100, including a processor 1101, a memory 1102, and a computer program stored in the memory 1102 and executable on the processor 1101. When the program is executed by the processor 1101, it implements the various processes of the above-described method embodiment for determining robot mapping error and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0233] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0234] This application also provides a non-transitory computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described method embodiment for determining robot mapping error and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0235] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0236] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for determining robot mapping errors.

[0237] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0238] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above-described method embodiment for determining robot mapping errors, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0239] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0240] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0241] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0242] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0243] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0244] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for determining robot mapping error, characterized in that, include: Obtain the robot's target path within a pre-defined indoor space; During the robot's movement along the target path, the system senses in real time whether there are obstacles within a preset range of the robot's current path, and generates a target point cloud map of the preset indoor space based on the sensing results. The obstacles include at least two specific markers, each a cube, with its side perpendicular or parallel to the wall of the preset indoor space. The system calculates the mapping error based on the first distance information corresponding to the distance between any two sides of the specific markers and / or between any side of the specific marker and the wall on the target point cloud map. If the mapping error is less than or equal to a preset threshold, the accuracy of the target point cloud map is determined to meet the requirements.

2. The method for determining robot mapping error according to claim 1, characterized in that, Before sensing in real time whether there are obstacles within a preset range of the robot's current travel path during the robot's movement along the target path, the method further includes: when the robot moves to the starting point of the target path, and a laser device perpendicularly set on the side of the wall or a specific marker emits a laser signal toward a reflecting device, adjusting the robot's pose so that the angle between the incident light path and the reflected light path of the laser signal does not exceed a target threshold, and determining the robot's pose in the current state as the acquisition pose; wherein the reflecting device is set on the robot and maintains the same height as the robot.

3. The method for determining robot mapping error according to claim 1, characterized in that, The step of calculating the mapping error based on the first distance information corresponding to the sides of any two specific landmarks and / or the side of any specific landmark and the wall on the target point cloud map includes: calculating at least one target error corresponding to any side based on the first distance information corresponding to the sides of any two specific landmarks and / or the side of any specific landmark and the wall on the target point cloud map; and calculating the mapping error based on the target errors corresponding to each side.

4. The method for determining robot mapping error according to claim 3, characterized in that, The step of calculating at least one target error corresponding to any one of the sides based on the first distance information corresponding to the sides of any two of the specific landmarks and / or the side of any one of the specific landmarks and the wall on the target point cloud map includes: calculating the target error based on the difference between the second distance information detected between the sides of any two of the specific landmarks and the wall and the first distance information.

5. The method for determining robot mapping error according to claim 3, characterized in that, The step of calculating at least one target error corresponding to any side based on the first distance information corresponding to the sides of any two specific landmarks and / or the side of any specific landmark and the wall on the target point cloud map includes: when there are multiple target point cloud maps, calculating the target error based on the difference between the first distance information corresponding to the sides of the same two specific landmarks and / or the side of the same specific landmark and the wall on any two target point cloud maps.

6. The method for determining robot mapping error according to claim 3, characterized in that, The step of calculating the mapping error based on the target error corresponding to each of the sides includes: determining the maximum value among at least one target error corresponding to the side as the first error corresponding to the side; and calculating the mapping error based on the average value of the first errors corresponding to each of the sides.

7. The method for determining robot mapping error according to any one of claims 1-6, characterized in that, The step of calculating the mapping error based on the first distance information corresponding to the sides of any two of the specific landmarks and / or the side of any one of the specific landmarks and the wall on the target point cloud map includes: when there are multiple target point cloud maps, updating the mapping error based on the average value of the mapping errors calculated from each of the target point cloud maps.

8. The method for determining robot mapping error according to any one of claims 1-6, characterized in that, The first distance information is determined according to the following steps: performing at least one filtering process and height truncation process on the coordinate data in the target point cloud map; performing side fitting on the remaining coordinate data to obtain the side centroid coordinates corresponding to each side and the wall; calculating the first distance information based on the side centroid coordinates corresponding to the sides of any two specific landmarks, and / or calculating the first distance information based on the side centroid coordinates corresponding to the side of any one of the specific landmarks and the wall.

9. A device for determining robot mapping error, characterized in that, include: The first processing module is used to obtain the target path of the robot in the preset indoor space; The second processing module is used to sense in real time whether there are obstacles within a preset range of the robot's current travel path during the robot's movement along the target path, and generate a target point cloud map of the preset indoor space based on the sensing results; wherein, the obstacles include at least two specific markers, the specific markers are cubic structures, and the sides of the specific markers are perpendicular or parallel to the walls of the preset indoor space; the third processing module is used to calculate the mapping error based on the first distance information corresponding to the sides of any two specific markers and / or the sides of any one specific marker and the wall on the target point cloud map; the fourth processing module is used to determine that the accuracy of the target point cloud map meets the requirements if the mapping error is less than or equal to a preset threshold.

10. A robot, characterized in that, The robot performs accuracy measurement based on the robot mapping error determination method as described in any one of claims 1-8.

11. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for determining robot mapping error as described in any one of claims 1-8.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for determining robot mapping error as described in any one of claims 1-8.

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