Map exception processing method and system for a self-moving device and self-moving device

Abstract

The application discloses a kind of map exception processing method, system and self-moving device of self-moving device.Based on the method, self-moving device in current task, in the case where it is detected that the target map currently established exists map data exception, can first obtain the point cloud data of current position;And according to the point cloud data of current position and the point cloud data of pre-stored sampling point saved when first point cloud map is established previously, it is determined whether the target map is really abnormal;In the case where it is determined that the target map is abnormal, previous establishment and save in self-moving device First point cloud map can be switched to use to carry out the navigation of current task in time.Thereby, the target map of map data exception can be avoided, accurately navigate and move, to successfully complete current task.

Description

Technical Field

[0001] This invention belongs to the field of robotics technology, specifically relating to a map anomaly handling method, system, and self-moving device for a self-moving device. Background Technology

[0002] Based on existing technology, the self-moving device will update and generate a new map in real time during each task; and use the new map for navigation to control the movement of the self-moving device in this task.

[0003] However, based on the above method, when the generated new map has anomalies, the self-device often cannot detect them in time and will continue to use the map for navigation, which makes the self-device prone to errors when navigating and affecting the execution of the task.

[0004] Therefore, it is necessary to improve the existing technology to overcome the aforementioned defects. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a map anomaly handling method for a self-moving device, comprising: when an anomaly is detected in the map data of the currently established target map, acquiring point cloud data of the current location; determining whether the target map is anomaly based on the point cloud data of the current location and the point cloud data of pre-stored sampling points; wherein, the point cloud data of the pre-stored sampling points is the point cloud data acquired by the self-moving device when establishing the point cloud map; the point cloud map is the map data established by the self-moving device when completing the first task in the target area; the self-moving device also stores the point cloud map; and when the target map is determined to be anomaly, switching to use the point cloud map for navigation in the current sub-task.

[0006] In one embodiment, determining whether the target map is abnormal based on the point cloud data of the current location and the point cloud data of pre-stored sampling points includes: obtaining target location parameters by mapping the point cloud data of the current location to the target map; determining the point cloud data of the corresponding reference sampling point from the point cloud data of the pre-stored sampling points based on the target location parameters; and determining whether the target map is abnormal based on the point cloud data of the current location and the point cloud data of the reference sampling point.

[0007] In one embodiment, determining the point cloud data of a corresponding reference sampling point from the point cloud data of pre-stored sampling points based on the target location parameters includes: locating the corresponding reference sampling point in the point cloud map based on the target location parameters; and obtaining the point cloud data of the reference sampling point from the point cloud data of pre-stored sampling points.

[0008] In one embodiment, determining whether the target map is abnormal based on the point cloud data of the current location and the point cloud data of a reference sampling point includes: calculating the difference value between the point cloud data of the current location and the point cloud data of the reference sampling point; comparing the difference value with a preset difference value threshold to obtain a comparison result; and determining that the target map is abnormal if the difference value is greater than the preset difference value threshold based on the comparison result.

[0009] In one embodiment, the method further includes: collecting state parameters of the self-mobile device; determining the current state of the self-mobile device based on the state parameters of the self-mobile device; detecting whether the current state matches a preset abnormal state; and determining that the target map currently being built has map data anomalies if the current state matches a preset abnormal state.

[0010] In one embodiment, the preset abnormal state includes at least one of the following: the self-moving device is in a suspended state, the wheels of the self-moving device are slipping, or the communication between the self-moving device and the base station is disconnected.

[0011] In one embodiment, after comparing the difference value with a preset difference value threshold to obtain a comparison result, the method further includes: determining that the target map is normal if the difference value is less than or equal to the preset difference value threshold based on the comparison result.

[0012] In one embodiment, if the target map is determined to be normal, the method further includes: using the target map for navigation in the current sub-task.

[0013] This invention provides a map anomaly handling system for a self-moving device, comprising: an acquisition module, configured to acquire point cloud data of the current location when an anomaly is detected in the currently established target map; a determination module, communicatively connected to the acquisition module, configured to determine whether the target map is anomaly based on the point cloud data of the current location and the point cloud data of pre-stored sampling points; wherein the point cloud data of the pre-stored sampling points is the point cloud data acquired by the self-moving device when establishing the point cloud map; the point cloud map is the map established by the self-moving device after completing a task in the target area; the self-moving device also stores the point cloud map; and a navigation module, communicatively connected to the determination module, configured to switch to using the point cloud map for navigation in the current task when the target map is determined to be anomaly.

[0014] The present invention also provides a self-moving device, comprising: a device body; and a controller disposed on the device body; wherein the controller is configured to: acquire point cloud data of the current location when an anomaly is detected in the currently established target map; determine whether the target map is abnormal based on the point cloud data of the current location and the point cloud data of pre-stored sampling points; wherein the point cloud data of the pre-stored sampling points is the point cloud data acquired by the self-moving device when establishing the point cloud map; the point cloud map is the map established by the self-moving device after completing a task in the target area; the self-moving device also stores the point cloud map; and switch to using the point cloud map for navigation of the current task when the target map is determined to be abnormal.

[0015] Based on the map anomaly handling method, system, and self-device provided by this invention, when performing the current sub-task, if the self-device detects an anomaly in the currently established target map, it can first acquire the point cloud data of the current location; and based on the point cloud data of the current location and the point cloud data of the pre-stored sampling points saved when the first point cloud map was previously established, determine whether the target map is truly anomaly; if the target map is determined to be anomaly, it can promptly switch to using the first point cloud map previously established and saved on the self-device for navigation in the current sub-task. This avoids using a target map with anomaly data, enabling accurate navigation and movement, and successfully completing the current sub-task. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This invention provides a method for handling map anomalies on self-moving devices.

[0018] Figure 2 A schematic diagram illustrating the structural composition of a map anomaly handling system for a self-moving device provided in an embodiment of the present invention;

[0019] Figure 3 This is a schematic diagram of the structural composition of a self-moving device provided in an embodiment of the present invention. Detailed Implementation

[0020] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The present invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0022] In this invention, unless otherwise stated, directional terms such as "upper," "lower," "top," and "bottom" are generally used in relation to the direction shown in the accompanying drawings, or in relation to the vertical, perpendicular, or gravitational direction of the component itself; similarly, for ease of understanding and description, "inner" and "outer" refer to the inner and outer contours of each component itself, but the above directional terms are not intended to limit this invention.

[0023] This embodiment provides a method for handling map anomalies on mobile devices. See also... Figure 1 As shown, the specific implementation of this method may include the following:

[0024] S101: If an anomaly is detected in the target map that has been established, obtain the point cloud data of the current location.

[0025] In some embodiments, the map anomaly handling method for the self-moving device described above can be specifically applied to the self-moving device. Specifically, the self-moving device may include self-moving cleaning devices, such as automatic sweeping robots, automatic mopping robots, etc. Furthermore, the self-moving device may also include self-moving inspection devices, nanny robots, etc.

[0026] Of course, it should be noted that the self-moving devices listed above are only illustrative. In practice, depending on the specific application scenario and processing requirements, the above methods can also be applied to other suitable types of self-moving robots or related devices in other scenarios.

[0027] In some embodiments, the target map may specifically be a newly generated map when the self-mobile device performs a current sub-task in the target area that is different from the first task.

[0028] Specifically, the target map mentioned above can be a newly created point cloud map (or LDS map) based on point cloud data acquired by LiDAR or laser sensor when the mobile device performs the current task in the target area.

[0029] Of course, it should be noted that the aforementioned target map may also include maps created by mobile devices through other devices or based on other positioning mechanisms.

[0030] In some embodiments, the aforementioned self-moving device may also be equipped with a laser direct oscillator (LDS) or a laser sensor.

[0031] In practice, the self-moving device can use the aforementioned lidar or laser sensor to acquire and construct a map of the target area based on point cloud data in the target area.

[0032] Specifically, for example, a self-moving device can emit laser signals by rotating at high speed in a target area; then, by measuring the time it takes for the laser signal to reflect back from an obstacle after it is emitted, the distance between the device and the obstacle in the area to be measured is determined, and a large amount of point cloud data carrying location information is obtained; then, based on the point cloud data, a corresponding map is constructed by determining the relative position.

[0033] In some embodiments, during the process of the self-moving device performing the first task in the target area, it typically collects point cloud data of multiple sampling points of the target area through LiDAR or LiDAR sensor; and then constructs a map of the target area based on the point cloud data of the multiple sampling points, which can be referred to as the first map.

[0034] In some embodiments, when constructing the first map, the self-moving device may employ a corresponding point cloud matching algorithm to construct a first map for the target area using the collected point cloud data.

[0035] In some embodiments, the point cloud matching algorithm may specifically include the ICP algorithm or the NDT algorithm, etc.

[0036] The ICP (Iterative Closest Point) algorithm described above finds the rotation parameter R and translation parameter T between the point cloud data to be registered and the reference cloud data, so that the two point cloud data satisfy the optimal matching under a certain metric criterion.

[0037] The aforementioned NDT (Normal Distributions Transform) algorithm is a registration algorithm applied to a statistical model of 3D points. It uses standard optimization techniques to determine the optimal match between two point cloud data. Because it does not utilize the features of the corresponding points for calculation and matching during the registration process, it consumes less processing time and is faster than other methods.

[0038] It should be noted that the point cloud matching algorithms listed above are merely illustrative. In actual implementation, other suitable point cloud algorithms may be used depending on the specific application scenario and processing requirements. This manual does not impose any limitations on this.

[0039] In some embodiments, the self-mobile device can save the point cloud data of multiple sampling points used to construct the first map collected during the first task in the target area to the local self-mobile device, denoted as the pre-stored sampling point point cloud data; at the same time, the self-mobile device can also save the first map to the local self-mobile device, denoted as the point cloud map.

[0040] In some embodiments, after the self-moving device completes the first task in the target area and saves the point cloud data and point cloud map of the aforementioned pre-stored sampling points, when performing a task again in the target area, in order to avoid map navigation errors caused by changes in the target area after the first task (e.g., obstacles in the target area have moved; or, adjacent areas of the target area have become accessible), a new map is regenerated through relevant devices (e.g., laser-type or other devices) during each task execution, and navigation is based on the new map.

[0041] Specifically, for example, while the mobile device is performing its current sub-task in the target area, it can continuously collect map data about the target area through relevant devices, and build and continuously update the currently built target map based on the collected data. Navigation is then performed based on this target map to complete the current sub-task.

[0042] In some embodiments, the target map established in the manner described above may contain map data anomalies, which could easily lead to errors if navigation continues based on the target map. Therefore, in this embodiment, when establishing the target map, the self-mobile device can detect whether there are map data anomalies in the currently established target map in real time or periodically (e.g., every 1 minute).

[0043] In some embodiments, the aforementioned self-moving device may also be equipped with state detection sensors, such as sensors for detecting the state of the wheels of the self-moving device, sensors for detecting the state of the body of the automated device, sensors for detecting the communication state between the self-moving device and the base station, etc.

[0044] Of course, the state detection sensors listed above are only illustrative. In actual implementation, depending on the specific application scenario and processing requirements, the state detection sensors may include other types of sensors. This specification does not limit this.

[0045] In some embodiments, the self-moving device can collect relevant status parameters through the aforementioned status detection sensor and detect whether there are map data anomalies in the currently established target map based on the status parameters.

[0046] In some embodiments, the method may further include the following:

[0047] S1: Status parameters collected from the mobile device;

[0048] S2: Determine the current state of the self-moving device based on the state parameters of the self-moving device;

[0049] S3: Detect whether the current state matches a preset abnormal state;

[0050] S4: If the current state matches the preset abnormal state, determine that the target map currently being built has a risk of map data abnormality.

[0051] Specifically, the aforementioned status parameters of the self-moving device may include one or more of the following: the self-moving device's body status parameters, the self-moving device's wheel status parameters, the self-moving device's communication status parameters, etc.

[0052] Correspondingly, the self-moving device can collect the status parameters of the self-moving device through the corresponding status detection sensors, and determine the current status of the self-moving device based on the aforementioned status parameters.

[0053] In some embodiments, the aforementioned preset abnormal state can be specifically understood as a state related to map data anomalies. For example, it could be a state that easily leads to map data anomalies, or a state caused by map data anomalies, etc.

[0054] In some embodiments, before implementation, a large amount of status data of mobile device map data when it is abnormal can be collected, and the status data can be clustered to obtain the preset abnormal status.

[0055] In some embodiments, the preset abnormal state may specifically include at least one of the following: the self-moving device is in a suspended state, the wheels of the self-moving device are slipping, the communication between the self-moving device and the base station is disconnected, etc.

[0056] Of course, it should be noted that the preset abnormal states listed above are only illustrative. In actual implementation, depending on the specific application scenario and accuracy requirements, the preset abnormal states may also include other states related to map data anomalies.

[0057] Through the above embodiments, it is possible to quickly and relatively quickly determine whether there are map data anomalies in the target map currently created by the mobile device with a relatively small amount of data processing.

[0058] In some embodiments, if the current state of the self-mobile device is detected to match a preset abnormal state, it can be determined that the target map established by the current self-mobile device is at risk of map data abnormality, that is, it is judged that the target map currently established is likely to be abnormal, and further more refined detection needs to be triggered to determine whether there is really an abnormality.

[0059] Conversely, if the current state of the self-device does not match the preset abnormal state, it can be determined that the target map established by the current self-device does not pose a risk of map data abnormality. In other words, it is judged that the target map currently established is highly likely to be normal and no further detection is required.

[0060] S102: Determine whether the target map is abnormal based on the point cloud data of the current location and the point cloud data of the pre-stored sampling points; wherein, the point cloud data of the pre-stored sampling points is the point cloud data obtained by the self-mobile device when building the point cloud map; the point cloud map is the map data built by the self-mobile device when completing the first task in the target area; the self-mobile device also stores the point cloud map.

[0061] In some embodiments, if anomalies are detected in the currently established target map, point cloud data at the current location can be collected using LiDAR or a laser sensor; then, the point cloud data at the current location and the point cloud data of pre-stored sampling points can be used for further, more refined detection to determine whether the currently established target map is truly abnormal.

[0062] In some embodiments, the aforementioned self-moving device locally stores a point cloud map created by the self-moving device when it completes the first task in the target area, as well as point cloud data of pre-stored sampling points collected and used when creating the point cloud map.

[0063] In some embodiments, determining whether the target map is abnormal based on the point cloud data of the current location and the point cloud data of pre-stored sampling points may include the following:

[0064] S1: Obtain the target location parameters by mapping the point cloud data of the current location to the target map;

[0065] S2: Based on the target location parameters, determine the corresponding reference sampling point's point cloud data from the pre-stored sampling point's point cloud data;

[0066] S3: Determine whether the target map is abnormal based on the point cloud data of the current location and the point cloud data of the reference sampling point.

[0067] In some embodiments, during specific implementation, the point cloud data of the current location can be mapped onto the target map according to a preset position coordinate transformation relationship to obtain the position coordinates based on the target map, which are then used as the aforementioned target position parameters.

[0068] In some embodiments, the point cloud data of each pre-stored sampling point in the stored pre-stored sampling point data corresponds to a sampling point with a location coordinate in the point cloud map.

[0069] In some embodiments, the above-mentioned determination of the corresponding reference sampling point's point cloud data from the pre-stored sampling point point cloud data based on the target location parameters may specifically include the following: locating the corresponding reference sampling point in the point cloud map based on the target location parameters; and obtaining the point cloud data of the reference sampling point from the pre-stored sampling point point cloud data.

[0070] In some embodiments, determining whether the target map is abnormal based on the point cloud data of the current location and the point cloud data of the reference sampling point may include the following:

[0071] S1: Calculate the difference between the point cloud data at the current location and the point cloud data at the reference sampling point;

[0072] S2: Compare the difference value with the preset difference value threshold to obtain the comparison result;

[0073] S3: Based on the comparison results, if the difference value is greater than a preset difference value threshold, the target map is determined to be abnormal.

[0074] In this embodiment, if the difference value is determined to be greater than a preset difference value threshold based on the comparison results, it can be judged that the currently established target map truly has a significant anomaly, which has a substantial impact on navigation accuracy. In this case, the reliability of the target map is poor, and subsequent navigation based on this target map has a high probability of resulting in navigation errors, affecting the execution of the current task.

[0075] In some embodiments, after comparing the difference value with a preset difference value threshold to obtain a comparison result, the method may further include: determining that the target map is normal if the difference value is less than or equal to the preset difference value threshold based on the comparison result.

[0076] In this embodiment, if the difference value is less than or equal to a preset difference value threshold based on the comparison results, it can be determined that the currently established target map does not actually have any anomalies, or that there are anomalies, but the anomalies are minor and have a limited impact on navigation accuracy. In this case, the reliability of the target map is still within a tolerable range, and navigation can be performed based on the target map subsequently.

[0077] Through the above embodiments, by collecting and utilizing point cloud data of the current location, combined with point cloud data of pre-stored sampling points, it is possible to efficiently and accurately detect and determine whether the currently established target map is truly abnormal.

[0078] In some embodiments, the determination of whether the target map is abnormal based on the point cloud data of the current location and the point cloud data of pre-stored sampling points may further include the following: determining the location parameters of the current location based on the target map, denoted as the first location parameter; mapping the point cloud data of the current location to the point cloud map to obtain the second location parameter; comparing the first location parameter and the second location parameter to obtain a comparison result; and, based on the comparison result, determining that the target map is abnormal if the difference between the first location parameter and the second location parameter is greater than a preset difference threshold; conversely, determining that the target map is normal if the difference between the first location parameter and the second location parameter is less than or equal to the preset difference threshold.

[0079] S103: If the target map is determined to be abnormal, switch to using the point cloud map for navigation in the current sub-task.

[0080] In some embodiments, if the target map is determined to be abnormal, navigation can be stopped using the currently created target map. Instead, the point cloud map created during the first task can be used to replace the target map for navigation, allowing the current task to continue. This effectively avoids navigation errors caused by continuing to use a target map with abnormal map data, which could affect the successful execution of the current task.

[0081] In some embodiments, when the target map is determined to be normal, the method may further include the following: using the target map for navigation in the current sub-task.

[0082] In some embodiments, if the target map is determined to be normal, navigation can continue using the target map to continue executing the current sub-task; and during the execution of the current sub-task, the target map continues to be updated in real time or periodically, while detecting whether there are any map data anomalies in the updated target map.

[0083] After completing the current task in the manner described above, and if no anomalies are detected in the target map, the target map for the current task can be obtained. Furthermore, the point cloud map saved during the execution of the first task can be updated using the target map of the current task, resulting in a more accurate and comprehensive updated point cloud map.

[0084] In some embodiments, the aforementioned current sub-task may specifically be a job task that covers the target area.

[0085] In some embodiments, corresponding to different application scenarios and different types of self-moving devices, the aforementioned current sub-task may include different types of job tasks covering target areas.

[0086] Specifically, for example, when the self-moving device is a self-moving cleaning device, the target area can be a room to be cleaned as specified by the user, and correspondingly, the current sub-task can include an automatic cleaning task covering that room. For example, for an automatic robot vacuum, the current sub-task can be a sweeping task covering the room to be cleaned.

[0087] For example, if the self-moving device is an inspection robot, the target area mentioned above can be the factory area to be inspected. Correspondingly, the current sub-task mentioned above can include automatic inspection tasks covering the factory area, etc.

[0088] Of course, it should be noted that the current sub-tasks listed above are only illustrative. In actual implementation, depending on the specific application scenario and processing requirements, the current sub-tasks may also include other types of job tasks.

[0089] Based on the map anomaly handling method for self-moving devices provided in the embodiments of this specification, when the self-moving device is performing the current sub-task, if it detects a risk of map data anomalies in the currently established target map, it can first acquire point cloud data of the current location; and based on the point cloud data of the current location and the point cloud data of pre-stored sampling points saved when the first point cloud map was previously established, it can determine whether the target map is truly anomaly; if the target map is determined to be anomaly, it can promptly switch to using the first point cloud map previously established and saved on the self-moving device for navigation in the current sub-task. This avoids using a target map with abnormal map data, allows for accurate navigation and movement, and ensures the smooth completion of the current sub-task.

[0090] This specification also provides an embodiment of a map anomaly handling system 200 for self-moving devices, which can be found in the following reference. Figure 2 As shown, the map anomaly handling system 200 for the self-mounted device may specifically include the following structural modules:

[0091] The acquisition module 201 can be used to acquire point cloud data at the current location when an anomaly is detected in the target map that has been established.

[0092] The determination module 202, which is communicatively connected to the acquisition module 201, is specifically used to determine whether the target map is abnormal based on the point cloud data of the current location and the point cloud data of pre-stored sampling points; wherein, the point cloud data of the pre-stored sampling points is the point cloud data acquired by the self-mobile device when building the point cloud map; the point cloud map is the map built by the self-mobile device after completing a task in the target area; the self-mobile device also stores the point cloud map;

[0093] The navigation module 203 is communicatively connected to the determination module 202. Specifically, it can be used to switch to the point cloud map for navigation in the current sub-task when the target map is determined to be abnormal.

[0094] In some embodiments, when the determining module 202 is specifically implemented, it can determine whether the target map is abnormal based on the point cloud data of the current location and the point cloud data of the pre-stored sampling points in the following manner: by mapping the point cloud data of the current location to the target map, the target location parameters are obtained; based on the target location parameters, the point cloud data of the corresponding reference sampling point is determined from the point cloud data of the pre-stored sampling points; based on the point cloud data of the current location and the point cloud data of the reference sampling points, it is determined whether the target map is abnormal.

[0095] In some embodiments, when the determining module 202 is specifically implemented, it can locate the corresponding reference sampling point in the point cloud map according to the target location parameters; and obtain the point cloud data of the reference sampling point from the point cloud data of the pre-stored sampling points.

[0096] In some embodiments, when the determining module 202 is specifically implemented, it can determine whether the target map is abnormal based on the point cloud data of the current location and the point cloud data of the reference sampling point in the following manner: calculate the difference value between the point cloud data of the current location and the point cloud data of the reference sampling point; compare the difference value with a preset difference value threshold to obtain a comparison result; and determine that the target map is abnormal if the difference value is greater than the preset difference value threshold based on the comparison result.

[0097] In some embodiments, the device may further include a detection module, which may be used to collect state parameters of the self-moving device; determine the current state of the self-moving device based on the state parameters of the self-moving device; detect whether the current state matches a preset abnormal state; and determine that the target map currently being built has a risk of map data abnormality if the current state matches the preset abnormal state.

[0098] In some embodiments, the preset abnormal state may specifically include at least one of the following: the self-moving device is in a suspended state, the wheels of the self-moving device are slipping, or the communication between the self-moving device and the base station is disconnected.

[0099] In some embodiments, when the determining module 202 is specifically implemented, after comparing the difference value with a preset difference value threshold and obtaining a comparison result, it can also be used to determine that the target map is normal if the difference value is less than or equal to the preset difference value threshold based on the comparison result.

[0100] In some embodiments, if the determination module 202 determines that the target map is normal, the device may continue to use the target map for navigation in the current sub-task.

[0101] Based on the map anomaly handling system for self-moving devices provided in the embodiments of this specification, when performing the current sub-task, if the system detects a risk of map data anomalies in the currently established target map, it can first acquire point cloud data of the current location; and based on the point cloud data of the current location and the point cloud data of pre-stored sampling points saved when the first point cloud map was previously established, it can determine whether the target map is truly anomaly; if the target map is determined to be anomaly, it can promptly switch to using the first point cloud map previously established and saved on the self-moving device for navigation in the current sub-task. This avoids using a target map with abnormal map data, allows for accurate navigation and movement, and ensures the smooth completion of the current sub-task.

[0102] This specification also provides a self-moving device, which can be found in the embodiments described herein. Figure 3 As shown, the self-moving device may include at least a device body 301 and a controller 302 disposed on the device body 301.

[0103] Specifically, the controller 302 can be used to: acquire point cloud data at the current location when an anomaly is detected in the currently established target map; determine whether the target map is abnormal based on the point cloud data at the current location and the point cloud data of pre-stored sampling points; wherein the point cloud data of the pre-stored sampling points is the point cloud data acquired by the self-mobile device when establishing the point cloud map; the point cloud map is the map established by the self-mobile device after completing a task in the target area; the self-mobile device also stores the point cloud map; and switch to using the point cloud map for navigation in the current task when the target map is determined to be abnormal.

[0104] In some embodiments, the aforementioned self-moving device may further include a laser sensor 303. The laser sensor 303 (or lidar) is used to acquire corresponding point cloud data by emitting laser signals.

[0105] This specification also provides a computer storage medium for the map anomaly handling method based on the above-described self-moving device. The computer storage medium stores computer program instructions, which, when executed, perform the following steps: When an anomaly is detected in the currently established target map, acquire point cloud data of the current location; determine whether the target map is abnormal based on the point cloud data of the current location and the point cloud data of pre-stored sampling points; wherein the point cloud data of the pre-stored sampling points is the point cloud data acquired by the self-moving device when establishing the point cloud map; the point cloud map is the map data established by the self-moving device after completing the first task in the target area; the self-moving device also stores the point cloud map; and when the target map is determined to be abnormal, switch to using the point cloud map for navigation in the current task.

[0106] In this embodiment, the storage medium includes, but is not limited to, Random Access Memory (RAM), Read-Only Memory (ROM), cache, hard disk drive (HDD), or memory card. The memory can be used to store computer program instructions. The network communication unit can be an interface configured according to standards specified in the communication protocol for network connection communication.

[0107] In this embodiment, the specific functions and effects implemented by the program instructions stored in the computer storage medium can be explained in comparison with other implementation methods, and will not be repeated here.

[0108] Obviously, the embodiments described above are merely some, not all, embodiments of the present invention. Based on the embodiments of the present invention, those skilled in the art can make other variations or modifications without creative effort, and all such variations or modifications should fall within the scope of protection of the present invention.

Claims

1. A method for handling map anomalies on self-moving devices, characterized in that, include: If the risk of map data anomalies is detected in the currently established target map, the point cloud data of the current location is obtained; Based on the point cloud data of the current location and the point cloud data of the pre-stored sampling points, it is determined whether the target map is abnormal; wherein, the point cloud data of the pre-stored sampling points is the point cloud data obtained by the self-mobile device when building the point cloud map; the point cloud map is the map data built by the self-mobile device when it completes the first task in the target area; the self-mobile device also stores the point cloud map. If the target map is determined to be abnormal, switch to using the point cloud map for navigation in the current sub-task; Anomalies in the target map can affect navigation accuracy.

2. The map anomaly handling method for self-moving devices according to claim 1, characterized in that, Based on the point cloud data of the current location and the point cloud data of pre-stored sampling points, determine whether the target map is abnormal, including: By mapping the point cloud data of the current location to the target map, the target location parameters are obtained; Based on the target location parameters, the point cloud data of the corresponding reference sampling point is determined from the point cloud data of the pre-stored sampling points; Based on the point cloud data of the current location and the point cloud data of the reference sampling point, determine whether the target map is abnormal.

3. The map anomaly handling method for self-moving devices according to claim 2, characterized in that, Based on the target location parameters, the corresponding reference sampling point's point cloud data is determined from the pre-stored sampling point point cloud data, including: Based on the target location parameters, the corresponding reference sampling point is located in the point cloud map; and the point cloud data of the reference sampling point is obtained from the point cloud data of the pre-stored sampling points.

4. The map anomaly handling method for self-moving devices according to claim 2, characterized in that, Based on the point cloud data of the current location and the point cloud data of the reference sampling point, determine whether the target map is abnormal, including: Calculate the difference between the point cloud data at the current location and the point cloud data at the reference sampling point; Compare the difference value with a preset difference value threshold to obtain a comparison result; Based on the comparison results, if the difference value is greater than a preset difference value threshold, the target map is determined to be abnormal.

5. The map anomaly handling method for self-moving devices according to claim 1, characterized in that, The method further includes: Status parameters collected from mobile devices; The current state of the self-moving device is determined based on its state parameters. Check if the current state matches a preset abnormal state; If the current state matches the preset abnormal state, it is determined that the target map currently being built is at risk of map data anomalies.

6. The map anomaly handling method for self-moving devices according to claim 5, characterized in that, The preset abnormal states include at least one of the following: the self-moving device is in a suspended state, the wheels of the self-moving device are slipping, or the communication between the self-moving device and the base station is disconnected.

7. The map anomaly handling method for self-moving devices according to claim 4, characterized in that, After comparing the difference value with a preset difference value threshold to obtain a comparison result, the method further includes: Based on the comparison results, if the difference value is less than or equal to a preset difference value threshold, the target map is determined to be normal.

8. The map anomaly handling method for self-moving devices according to claim 7, characterized in that, If the target map is determined to be normal, the method further includes: Use the target map for navigation in the current sub-task.

9. A map anomaly handling system for self-moving devices, characterized in that, include: The acquisition module is used to acquire point cloud data of the current location when the risk of map data anomalies is detected in the currently established target map; The determination module, which is communicatively connected to the acquisition module, is used to determine whether the target map is abnormal based on the point cloud data of the current location and the point cloud data of pre-stored sampling points; wherein, the point cloud data of the pre-stored sampling points is the point cloud data acquired by the self-mobile device when building the point cloud map; the point cloud map is the map built by the self-mobile device after completing a task in the target area; the self-mobile device also stores the point cloud map. The navigation module is communicatively connected to the determination module and is used to switch to the point cloud map for navigation in the current sub-task if the target map is determined to be abnormal. Anomalies in the target map can affect navigation accuracy.

10. A self-moving device, characterized in that, include: Equipment body; The controller is located on the main body of the device; The controller is used for: If the risk of map data anomalies is detected in the currently established target map, the point cloud data of the current location is obtained; Based on the point cloud data of the current location and the point cloud data of pre-stored sampling points, it is determined whether the target map is abnormal; wherein, the point cloud data of the pre-stored sampling points is the point cloud data obtained by the self-mobile device when building the point cloud map; the point cloud map is the map built by the self-mobile device after completing a task in the target area; the self-mobile device also stores the point cloud map. If the target map is determined to be abnormal, switch to using the point cloud map for navigation in the current sub-task; Anomalies in the target map can affect navigation accuracy.

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