Cleaning equipment obstacle avoidance method and device, cleaning equipment and readable storage medium

Abstract

The invention provides a cleaning equipment obstacle avoidance method and device, cleaning equipment and a readable storage medium, and relates to the field of smart home. The cleaning equipment comprises a structured light module and a laser radar, and the method comprises the following steps: obtaining first point cloud data through the structured light module, and obtaining second point cloud data through the laser radar; marking a first map corresponding to the structured light module based on the first point cloud data, and marking a second map corresponding to the laser radar based on the second point cloud data; and performing obstacle recognition on the first map and the second map to obtain an obstacle recognition result, and controlling the movement of the cleaning equipment according to the obstacle recognition result.

Description

Technical Field

[0001] The present application relates to the field of smart home, and in particular to a cleaning equipment obstacle avoidance method, device, cleaning equipment and readable storage medium. Background Art

[0002] Currently, collision avoidance has become an essential function for cleaning robots. To improve the cleaning area and cleaning effect, cleaning robots will inevitably encounter various obstacles and objects to avoid during the cleaning process. For these objects, cleaning robots usually take actions such as circumventing them by following the edge or retreating to avoid them.

[0003] The cleaning robot in the related art uses information from a single sensor to detect obstacles, but the detection accuracy of this method is insufficient, resulting in the cleaning robot being unable to travel smoothly. Summary of the Invention

[0004] In view of this, the present application provides a cleaning equipment obstacle avoidance method, device, cleaning equipment and readable storage medium, which solves the problem of insufficient detection accuracy caused by obstacle detection based on a single sensor in the related art.

[0005] In a first aspect, an embodiment of the present application provides an obstacle avoidance method for a cleaning device, wherein the cleaning device includes a structured light module and a laser radar, and the method includes:

[0006] Acquire first point cloud data through the structured light module, and acquire second point cloud data through the laser radar;

[0007] Marking a first map corresponding to the structured light module based on the first point cloud data, and marking a second map corresponding to the laser radar based on the second point cloud data;

[0008] Obstacle identification is performed on the first map and the second map to obtain an obstacle identification result, and the movement of the cleaning device is controlled according to the obstacle identification result.

[0009] In a second aspect, an embodiment of the present application provides an obstacle avoidance device for cleaning equipment, wherein the cleaning equipment includes a structured light module and a laser radar, and the device includes:

[0010] A data acquisition module, configured to acquire first point cloud data through the structured light module and second point cloud data through the laser radar;

[0011] a map marking module, configured to mark a first map corresponding to the structured light module based on the first point cloud data, and mark a second map corresponding to the laser radar based on the second point cloud data;

[0012] an obstacle identification module, configured to perform obstacle identification on the first map and the second map to obtain an obstacle identification result;

[0013] The obstacle avoidance control module is used to control the movement of the cleaning device according to the obstacle recognition result.

[0014] In a third aspect, an embodiment of the present application provides a cleaning device, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the method of the first aspect are implemented.

[0015] In a fourth aspect, an embodiment of the present application provides a readable storage medium, which stores a program or instruction. When the program or instruction is executed by a processor, the steps of the method of the first aspect are implemented.

[0016] In an embodiment of the present application, the first point cloud data is obtained by a structured light module, and the second point cloud data is obtained by a laser radar. Map marking is performed based on the first point cloud data to obtain a first map, and map marking is performed based on the second point cloud data to obtain a second map. Obstacle identification is performed based on the marked first map and the second map to obtain an obstacle identification result. Finally, the cleaning equipment is controlled to avoid the movement of the obstacle position according to the obstacle identification result. In an embodiment of the present application, map marking is performed based on the point cloud generated by the structured light module and the laser radar, and obstacle identification is performed based on the marked map. Map marking generated by different ranging sensors is realized to determine whether there is an obstacle ahead, thereby performing obstacle avoidance-related actions and improving the accuracy of obstacle avoidance actions.

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 A schematic diagram showing a flow chart of a cleaning equipment obstacle avoidance method according to an embodiment of the present application is shown;

[0020] Figure 2 A schematic diagram showing obstacle position markings in an obstacle persistent map according to an embodiment of the present application is shown;

[0021] Figure 3A schematic diagram showing a cleaning device according to an embodiment of the present application that cannot enter a low space of a critical height;

[0022] Figure 4 A structural block diagram of a cleaning equipment obstacle avoidance device according to an embodiment of the present application is shown;

[0023] Figure 5 A structural block diagram of a cleaning device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0024] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0025] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

[0026] The cleaning equipment obstacle avoidance method, device, cleaning equipment and readable storage medium provided in the embodiments of the present application are described in detail below with reference to the accompanying drawings through specific embodiments and their application scenarios.

[0027] The embodiments of the present application provide a cleaning device, which includes a sweeper, a mop, a sweeper and mop all-in-one, a household machine, etc., and can realize functions such as floor cleaning, item organization, real-time home monitoring, and pet companionship. The cleaning device includes a device body and a walking assembly installed on the device body, and the walking assembly includes a walking wheel and a universal wheel. The device body can realize linear motion along the x-axis and y-axis directions of the plane, linear motion along the z-axis direction, and rotation along the z-axis direction through the walking wheel and the universal wheel. The linear motion of the device body along the z-axis direction, that is, the adjustment of the height of the device body from the ground, can be achieved through the walking wheel and / or the universal wheel. The walking wheel and the universal wheel can be raised and lowered separately or together to achieve the adjustment of the height of the device body from the ground.

[0028] The cleaning equipment also includes a structured light module and a laser radar for object detection. The structured light module mainly relies on the surface structured light emitted by the laser emitter to perform distance measurement and modeling of three-dimensional information in space, while the laser radar relies on a modulated laser beam to complete the distance measurement. The embodiment of the present application does not limit the installation angle and installation position of the structured light module. The structured light module can include at least one of a single-line module and a dual-line module. The single-line module refers to a module with one emission source, and the emission direction is slightly downward toward the location. The dual-line module refers to a module with two emission sources, and the laser blade of its structured light will be perpendicular to the ground. The height detected by the dual-line module will be higher than that detected by the single-line module.

[0029] An embodiment of the present application provides an obstacle avoidance method for cleaning equipment, which is applied to the cleaning equipment of the above embodiment and can comprehensively utilize the structured light module and laser radar of the cleaning equipment to perform high-precision obstacle detection, thereby improving the accuracy of the cleaning equipment in avoiding obstacles.

[0030] like Figure 1 As shown, the method includes:

[0031] Step 101: Acquire first point cloud data through a structured light module, and acquire second point cloud data through a laser radar.

[0032] In this step, the structured light module calculates the three-dimensional coordinates by projecting a known pattern and capturing changes in the reflected pattern, generating the first point cloud data. The lidar measures the distance and generates the second point cloud data by emitting laser pulses and receiving reflected signals.

[0033] Step 102: Mark a first map corresponding to the structured light module based on the first point cloud data, and mark a second map corresponding to the lidar based on the second point cloud data.

[0034] In this step, map marking is performed based on the first point cloud data to obtain a first map, and map marking is performed based on the second point cloud data to obtain a second map.

[0035] It should be noted that the structured light module and the lidar each have their own corresponding maps. For example, if there are N single-line modules, M dual-line modules, and K lidar sensors, the number of maps is N+M+K. The format of the map can be a map and a parent-child map. In the parent-child map, the sub-map has a high resolution and a small range; the parent map has a low resolution and a large range. The value of each grid in the map represents the probability that the grid is occupied by an obstacle. The value of the grid is an integer, and the integer range is 0 to 255, that is, the lower limit of the grid value is 0, and the upper limit of the grid value is 255. Among them, 0 means that the grid is completely confident that it is an obstacle, and 255 means that the grid is completely confident that there is no obstacle.

[0036] To facilitate subsequent obstacle detection, a pre-set obstacle detection threshold a can be set based on business needs. a can be greater than 0 and less than 0.5. When performing subsequent obstacle detection, if the grid value is ≤ a × 255 (the product of the obstacle detection threshold and the upper limit of the grid value), it is confirmed to be an obstacle. If the grid value is greater than (1-a) × 255, it is confirmed to be free of obstacles.

[0037] In addition, to facilitate map coloring, a ground height threshold g is pre-set, typically based on the average ground height or the ground height of a specific area. By setting the ground height threshold g, it is possible to distinguish which point cloud data belongs to the ground and which belongs to obstacles.

[0038] In one embodiment of the present application, marking a first map corresponding to the structured light module based on the first point cloud data includes:

[0039] Establishing a first map corresponding to the structured light module, the first map including a plurality of grids, wherein the numerical value of the grid represents the confidence level that the grid is an obstacle; the first map is a first grid map;

[0040] Determining a first empty area grid and a first obstacle grid in the first map based on the three-dimensional coordinates of the first point cloud data in the three-dimensional space and a preset ground height threshold;

[0041] The first empty area grid is subjected to an empty area filling process, and the first obstacle grid is subjected to an obstacle coloring process, so as to achieve marking of the first map.

[0042] In this embodiment, a first map corresponding to the structured light module is established. For each point cloud data emitted by the structured light module, the three-dimensional coordinates (x, y, z) of the point cloud in three-dimensional space can be obtained based on the principle of triangulation. Based on the three-dimensional coordinates and the preset ground height threshold, the first empty area grid and the first obstacle grid in the first map are determined. The empty area grid is characterized as a grid without obstacles, and the obstacle grid is characterized as a grid with obstacles. Furthermore, the first empty area grid is subjected to an empty area painting process, and the first obstacle grid is subjected to an obstacle painting process, thereby achieving the elimination of empty areas and the painting of obstacles, and completing the obstacle map update of the first map corresponding to the structured light module.

[0043] In one embodiment of the present application, determining a first empty area grid and a first obstacle grid in a first map based on three-dimensional coordinates of first point cloud data in three-dimensional space and a preset ground height threshold includes:

[0044] Based on the three-dimensional coordinates of the first point cloud data, determine the first point cloud data below the ground height threshold, record it as the first target point cloud data, and determine the first point cloud data above the ground height threshold, record it as the second target point cloud data;

[0045] The grid occupied by the first target point cloud data in the first map is determined to be a first empty area grid, and the grid occupied by the second target point cloud data in the first map is determined to be a first obstacle grid.

[0046] In this embodiment, the z coordinate of each first point cloud data item in the three-dimensional coordinates is compared with a preset ground height threshold g. First point cloud data items with a height less than the ground height threshold are recorded as first target point cloud data, and first point cloud data items with a height greater than or equal to the ground height threshold are recorded as second target point cloud data. In one embodiment, first point cloud data items with a height greater than or equal to the ground height threshold and less than the height h of the cleaning equipment are recorded as second target point cloud data. By limiting the height h of the cleaning equipment, it is possible to prevent drifting ground point clouds from being mistakenly identified as obstacles, and to avoid misidentification of obstacles due to terrain changes such as bumps and depressions on the ground, thereby improving the accuracy of the judgment.

[0047] Furthermore, the grids occupied by the first target point cloud data in the first map are used as first empty area grids, and the grids occupied by the second target point cloud data in the first map are used as first obstacle grids.

[0048] The embodiment of the present application can accurately mark obstacles on the first map, providing a precise data basis for the subsequent obstacle avoidance control of the cleaning equipment.

[0049] In one embodiment of the present application, marking a second map corresponding to the laser radar based on the second point cloud data includes:

[0050] Establishing a second map corresponding to the laser radar, the second map including a plurality of grids, wherein the numerical value of the grid represents the confidence level that the grid is an obstacle; the second map is a second grid map;

[0051] Convert the initial coordinates of the second point cloud data acquired by the laser radar into two-dimensional coordinates in two-dimensional space. The initial coordinates of the second point cloud data are marked as (range_i, angle_i, laserPose), where range_i represents the distance of the i-th point cloud from the center of the laser radar, angle_i represents the angle formed by the i-th point cloud and the 0-degree ray of the laser radar, and laserPose represents the pose data of the laser radar.

[0052] Determine a second empty area grid and a second obstacle grid between the target grid occupied by the laser radar and the two-dimensional coordinates;

[0053] The second empty area grid is subjected to an empty area filling process, and the second obstacle grid is subjected to an obstacle coloring process, so as to realize marking of the second map.

[0054] In this embodiment, a second map corresponding to the laser radar is established, and the initial coordinates of the second point cloud data obtained by the laser radar are converted into two-dimensional coordinates in a two-dimensional space. Specifically, the laser ray emitted by the laser radar is parallel to the ground. In design, the height of the obstacle should be consistent with the height of the transmitting end of the laser radar. The second point cloud data P_i obtained can usually be expressed as (range_i, angle_i, laserPose), where range_i represents the distance of the i-th point cloud from the center of the laser radar, angle_i represents the angle formed by the i-th point cloud and the 0-degree ray of the laser radar, and laserPose represents the posture data of the laser radar, which includes the coordinates of the laser radar and the orientation angle of the 0-degree ray. For each second point cloud data P_i (range_i, angle_i, laserPose) generated by the laser radar, the two-dimensional coordinates (x_i, y_i) of its point cloud P_i on the two-dimensional plane can be obtained.

[0055] After obtaining the 2D coordinates of the point cloud, perform the operations of clearing empty areas and coloring obstacles on the second map. Specifically, for each point cloud P_i, draw a line segment from the grid where the LiDAR is located (i.e., the target grid (xLaserPose, yLaserPose)) to (x_i, y_i). Traverse each grid (Cell_i) covered by this line segment to obtain the second empty area grid and the second obstacle grid.

[0056] Furthermore, the second empty area grid is processed to be empty, and the second obstacle grid is processed to be obstacle colored, thereby achieving the elimination of empty areas and the coloring of obstacles, and completing the obstacle map update of the second map corresponding to the laser radar.

[0057] In one embodiment of the present application, determining a second empty area grid and a second obstacle grid between the target grid occupied by the laser radar and the two-dimensional coordinates includes:

[0058] The grid between the target grid and the two-dimensional coordinate is used as the second empty area grid, and the grid to which the two-dimensional coordinate belongs is used as the second obstacle grid.

[0059] In this embodiment, for each point cloud P_i, a line segment is drawn from the target grid (xLaserPose, yLaserPose) where the laser radar is located to (x_i, y_i). Each grid (Cell_i) covered by this line segment is traversed. These grids can be divided into two categories: one is the empty area between the target grid and (x_i, y_i), which is the second empty area grid; the other is the obstacle point belonging to (x_i, y_i), which is the second obstacle grid.

[0060] The embodiment of the present application can accurately mark obstacles on the second map, providing a precise data basis for the subsequent obstacle avoidance control of the cleaning equipment.

[0061] In one embodiment of the present application, performing a blanking process on the blank area grid includes:

[0062] Update the values of the empty area grid according to the empty area elimination formula; the empty area elimination formula is:

[0063] CellValueAfter_i=CellValue_i×ChangeRate_1+(1-ChangeRate_1)×u1

[0064] Among them, CellValueAfter_i is the value of the empty area grid after the update, CellValue_i is the value of the empty area grid before the update, ChangeRate_1 is the preset empty area change rate, and u1 is the upper limit of the grid value;

[0065] Perform obstacle coloring on the obstacle grid, including:

[0066] Update the value of the obstacle grid according to the obstacle coloring formula; the obstacle coloring formula is:

[0067] CellValueAfter_i'=CellValue_i'×ChangeRate_2+(1-ChangeRate_2)×u2

[0068] Among them, CellValueAfter_i' is the value of the obstacle grid after the update, CellValue_i' is the value of the obstacle grid before the update, ChangeRate_2 is the preset obstacle change rate, and u2 is the lower limit of the grid value.

[0069] In this embodiment, performing the empty area filling processing on the first empty area grid and the second empty area grid includes updating the values of the empty area grid according to the empty area elimination formula, and performing the obstacle coloring processing on the first obstacle grid and the second obstacle grid includes updating the values of the obstacle grid according to the obstacle coloring formula.

[0070] Among them, the empty space change rate ChangeRate_1 and the obstacle coating change rate ChangeRate_2 are preset. The empty space change rate ChangeRate_1 is the rate parameter for removing obstacles, and the obstacle coating change rate ChangeRate_2 is the rate parameter for adding obstacles. These two values are empirical values.

[0071] Taking any empty area grid in the first empty area grid and the second empty area grid as an example, in the empty area elimination stage, all empty area grids constitute a grid list L_empty that needs to be filled with empty areas. For each grid CellEmpty_i in L_empty, the value of the grid before the emptying operation is CellValue_i, and after the emptying operation is CellValueAfter_i, then:

[0072] CellValueAfter_i=CellValue_i×ChangeRate_1+(1-ChangeRate_1)×u1

[0073] Here, u1 is the upper limit of the grid value, for example, 255.

[0074] Taking any obstacle grid in the first and second obstacle grids as an example, in the obstacle coloring stage, all obstacle grids constitute a grid list L_empty' that needs to be colored. For each grid CellEmpty_i' in L_empty', the value of the grid before the coloring operation is CellValue_i', and after the coloring operation is CellValueAfter_i', then:

[0075] CellValueAfter_i'=CellValue_i'×ChangeRate_2+(1-ChangeRate_2)×u2

[0076] Here, u2 is the lower limit of the grid value, for example, 0.

[0077] Through the above method, the empty area grid is filled with blanks, and the obstacle grid is colored with colors, thus completing the marking of the map.

[0078] Step 103 : performing obstacle recognition on the first map and the second map to obtain an obstacle recognition result, and controlling the movement of the cleaning device according to the obstacle recognition result.

[0079] In this embodiment, obstacle recognition is performed based on the marked first map and the second map to obtain an obstacle recognition result. Finally, the cleaning device is controlled to move to avoid the obstacle according to the obstacle recognition result.

[0080] It should be noted that obstacle identification can be performed on the first map and the second map respectively. If it is determined that there is an obstacle based on the first map, and if it is also determined that there is an obstacle based on the second map, it is determined that there is indeed an obstacle. If it is determined that there is an obstacle based on one of the first map and the second map, and the other determines that there is no obstacle, the judgment result of the sensor with a higher priority set by the user shall prevail. For example, if the user sets the sensor with a higher priority to be a structured light module, then the judgment result of the first map shall prevail; if the user sets the sensor with a higher priority to be a lidar, then the judgment result of the second map shall prevail. Alternatively, continue to detect obstacles in the manner described later using the collision sensor of the cleaning equipment to detect obstacles.

[0081] In an embodiment of the present application, map marking is performed based on the point cloud generated by the structured light module and the lidar, and obstacle identification is performed based on the marked map. Map markings generated by different ranging sensors are realized to determine whether there are obstacles ahead, thereby performing obstacle avoidance-related actions and improving the accuracy of obstacle avoidance actions.

[0082] LiDAR has a limited detection height, for example, it can only detect horizontal surfaces 9 cm high, but it has high ranging accuracy and very little noise. The structured light module can observe both low objects and the features of tall objects, but it has low ranging accuracy and high noise. In the embodiment of the present application, through the mutual cooperation of the two, the height measurement data can be processed with the help of the blade characteristics of structured light and the category of obstacles can be marked; for LiDAR, its advantage of accurate ranging can be used to obtain a more accurate ranging distance. The comprehensive use of structured light modules and LiDAR for high-precision obstacle detection improves the accuracy of obstacle avoidance.

[0083] In one embodiment of the present application, obstacle recognition is performed on the first map and the second map to obtain an obstacle recognition result, including:

[0084] For at least one of the first map and the second map, a determination is made as to whether there is a suspected obstacle area within a surrounding area with a preset distance as a radius and the position of the cleaning device as the center, and a first obstacle identification result is obtained. The suspected obstacle area is a grid whose value is ≤ a preset threshold value, and the preset threshold value is equal to the product of a preset obstacle determination threshold value and an upper limit value of the grid value.

[0085] In this embodiment, suspected obstacles can be identified on any one of the first map and the second map, that is, both the structured light module and the lidar can identify suspected obstacles according to the suspected obstacle identification method defined in this application.

[0086] Specifically, the position (x_i', y_i', angle_i') of the cleaning device on the map is determined. A surrounding area centered on (x_i', y_i') and with a preset distance, such as 1 meter, is traversed. Grids with values ≤ a preset threshold are extracted. The preset threshold is equal to the product of a preset obstacle determination threshold and an upper limit of the grid values. For example, grids with values less than or equal to the obstacle determination threshold a×255 are extracted to obtain a first obstacle identification result. The first obstacle identification result includes the presence of a suspected obstacle area in the surrounding area or the absence of a suspected obstacle area in the surrounding area.

[0087] By the above-mentioned method, it is possible to perform preliminary identification of whether there are obstacles in the area surrounding the cleaning device in the first map and / or the second map.

[0088] It should be noted that suspected obstacle identification is performed on both the first and second maps. If a suspected obstacle area is determined to exist based on the first map and also based on the second map, then an obstacle area is determined to exist. If a suspected obstacle area is determined to exist based on one of the first and second maps, but not based on the other, the result of the sensor with the higher user-set priority will prevail. Alternatively, obstacle detection can continue using the cleaning equipment's collision sensor as described below.

[0089] In one embodiment of the present application, if a grid is found on the map that is less than or equal to a preset threshold, it is first marked as a suspected obstacle, and then a determination is made as to whether it is a real obstacle or a low space that should not hinder the entry of cleaning equipment. Specifically, obstacle identification is performed on the first map and the second map to obtain an obstacle identification result, which also includes:

[0090] If the first obstacle identification result is that there is a suspected obstacle area in the surrounding area, the collision sensor of the cleaning equipment is used to detect the obstacle to determine the location of the obstacle; wherein, the way the collision sensor detects the obstacle includes: controlling the cleaning equipment to slow down and move, and determining an obstacle persistence map in the first map and / or the second map. When the collision sensor of the cleaning equipment is triggered, the current position of the cleaning equipment and the current position of the collision sensor are marked in the obstacle persistence map as the obstacle position.

[0091] In this embodiment, if it is determined based on the first obstacle recognition result that there is a suspected obstacle area in the area surrounding the cleaning device, the collision sensor of the cleaning device is continued to be used to detect obstacles, and the obstacle detection is performed. Specifically, the following steps are included:

[0092] (1) Control the cleaning equipment to slow down. In the direction of the cleaning equipment's movement, if a query in the LiDAR map and the structured light module map yields a grid that is less than or equal to a preset threshold, it indicates that this area is a suspected obstacle area, and the cleaning equipment will slow down.

[0093] (2) Determine a persistent obstacle map. The cleaning device will persistently record a persistent obstacle map. For example, a 5cm×5cm precision map that combines a lidar map and a structured light module map can be saved for subsequent exploration and recording. Here, the persistent obstacle map can be a map that is a fusion of the first map and the second map.

[0094] (3) Collision sensor impact record. During the deceleration process, if the collision sensor of the cleaning device is triggered at a certain moment, the position of the cleaning device at the time of the current trigger and the sensor position of the collision sensor will be marked on the obstacle persistent map. The sensor position includes the position of the sensor on the left, middle, and right of the cleaning device. For example, Figure 2 As shown, after the right collision sensor of the cleaning equipment is triggered, the obstacle position is marked in the obstacle persistent map.

[0095] It should be noted that if the collision sensor of the cleaning device is not hit at a certain moment, it indicates that there is no obstacle in the suspected obstacle area. Furthermore, based on the cleaning device's position at that moment, the existing obstacle location markers in the area covered by the cleaning device's body in the persistent obstacle map will be cleared, allowing the obstacle location markers to be updated to ensure their accuracy.

[0096] In the embodiment of the present application, for suspected obstacle areas, obstacle detection is performed based on the collision sensor of the cleaning equipment to determine whether it is a real obstacle, thereby improving the accuracy of obstacle detection and ensuring the reliability of obstacle avoidance control.

[0097] In one embodiment of the present application, the method further includes: obtaining the obstacle position marked by the user, and updating the marked obstacle position in the obstacle persistent map according to the obstacle position marked by the user.

[0098] In this embodiment, the user can edit the map. For example, the user can mark obstacles on the map in the APP of the user terminal. Obtain the obstacle location marked by the user, and update the marked obstacle location in the persistent obstacle map according to the obstacle location marked by the user.

[0099] For example, an automatic marking value of 0 indicates that the collision sensor has been triggered and it is an obstacle; an automatic marking value of 1 indicates that it is not an obstacle. The user can modify the automatic marking results by marking whether there is an obstacle in the persistent obstacle map. If there is an obstacle, the mark is 2, and if there is no obstacle, the mark is 1.

[0100] In the embodiment of the present application, the user can correct the obstacles automatically marked by the cleaning equipment, thereby improving the accuracy of obstacle determination.

[0101] For cleaning equipment equipped with a liftable laser radar, the upper external sensor of the cleaning equipment measures the upper space of the fuselage. If the upper side height is lower than a certain threshold h_lowArea, it will be determined to have entered a low space, and the liftable laser radar will automatically retract into the fuselage, making it easier for the fuselage to shuttle through the low space. Figure 3 As shown in the figure, when the elevating LiDAR is raised, it is at a higher level and will mistake a low space at a critical height for an obstacle. If the point cloud data collected by the LiDAR preliminarily identifies a low space at a critical height as an obstacle, the cleaning equipment will trigger an obstacle avoidance maneuver before entering the low space. The upper sensor inside the fuselage will not be able to obtain information about the low space and cannot enter the low space.

[0102] To this end, in one embodiment of the present application, for cleaning equipment with a liftable lidar, the height information of the structured light module is used to detect whether the obstacle determined by the lidar is suspended or a solid obstacle, thereby deciding whether the cleaning equipment should perform obstacle avoidance or attempt to cross. Specifically, obstacle recognition is performed on the first map and the second map to obtain the obstacle recognition result, which also includes:

[0103] For the target suspected obstacle area in the second map, determining a corresponding area of the target suspected obstacle area in the first map;

[0104] In the first map, determining whether there is a suspected obstacle area within a first preset range of the corresponding area, and obtaining a determination result;

[0105] According to the judgment result, the target suspected obstacle area is determined to be an obstacle position or a low space;

[0106] Control the movement of the cleaning equipment based on the obstacle recognition results, including:

[0107] If the target suspected obstacle area is determined to be the obstacle location, the cleaning equipment is controlled to move to avoid the obstacle location;

[0108] If the target suspected obstacle area is determined to be a low area, the cleaning equipment is controlled to pass through the low area after the laser radar descends.

[0109] In this embodiment, the first map corresponding to the structured light module is used to verify suspected obstacle areas in the second map corresponding to the laser radar to determine whether they are real obstacles or low spaces of a critical height. The low area refers to the ground area corresponding to the suspended space formed between the bottom of furniture such as beds, TV cabinets, and sofas and the ground. The height of the low area is higher than the first height and lower than the second height. The first height is the height from the bottom of the elevating laser radar to the top of the elevating laser radar when the elevating laser radar is in the lowered state. The second height is the sum of the third height and the preset height. The third height is the height from the bottom of the elevating laser radar to the top of the elevating laser radar when the elevating laser radar is in the raised state. The preset height can be 0 to 10 cm, preferably 3 to 5 cm.

[0110] For the target suspected obstacle area in the second map, the area corresponding to the target suspected obstacle area is determined in the first map, that is, the area with the same coordinate range. In the first map, a determination is made as to whether a suspected obstacle area exists within a first preset range of the corresponding area, a determination result is obtained, and based on the determination result, the target suspected obstacle area is determined to be an obstacle location or a low space. For example, if the determination result shows that a suspected obstacle area exists within the first preset range of the corresponding area, the target suspected obstacle area is determined to be an obstacle location; if the determination result shows that no suspected obstacle area exists within the first preset range of the corresponding area, the target suspected obstacle area is determined to be a low space.

[0111] Furthermore, if the target suspected obstacle area is determined to be an obstacle position, the cleaning device is controlled to move to avoid the obstacle position; if the target suspected obstacle area is determined to be a low area, the cleaning device is controlled to pass through the low area after the laser radar descends.

[0112] The embodiment of the present application can distinguish between real obstacles and low spaces at a critical height of the cleaning equipment, thereby preventing the cleaning equipment from identifying the low spaces at the critical height as obstacles and being unable to enter the low spaces for cleaning.

[0113] In one embodiment of the present application, if the first map is obtained by a structured light module of a single-line module and a dual-line module, determining that the target suspected obstacle area is an obstacle location or a low space according to the judgment result includes:

[0114] If the judgment result is that there is a suspected obstacle area within the first preset range of the corresponding area, determining the target suspected obstacle area in the second map as the obstacle location;

[0115] If the judgment result is that there is no suspected obstacle area within the first preset range of the corresponding area, the target suspected obstacle area in the second map is determined to be a low area with a height similar to that of the laser radar in the raised state.

[0116] In this embodiment, the structured light module includes a single-line module and a dual-line module. In this case, the structured light module can detect both low and high objects, and the first map is obtained by the single-line module and the dual-line module. If on the first map, there is a suspected obstacle area within the first preset range of the corresponding area corresponding to the target suspected obstacle area of the second map, that is, both the second map and the first map determine that it is a suspected obstacle area, then the target suspected obstacle area is determined to be a real obstacle. If on the first map, there is no suspected obstacle area within the first preset range of the corresponding area corresponding to the target suspected obstacle area of the second map, that is, the second map determines that the target suspected obstacle area is a real obstacle, and the first map determines that it is not a real obstacle, that is, the obstacle is suspended, then the target suspected obstacle area is determined to be a low area with a height similar to that of the laser radar in the raised state.

[0117] For example, a cleaning device fully equipped with single- and dual-line structured light can identify an obstacle if a cell (cellX_i, cellY_i) has a value ≤ the obstacle determination threshold a×255 within a 2cm area around the corresponding location in the single- and dual-line maps. The obstacle is considered to be an actual obstacle and obstacle avoidance is performed. Otherwise, the obstacle is considered to be in a low area at the critical height of the LiDAR and can be passed.

[0118] In one embodiment of the present application, if the first map is acquired by a structured light module of a single-line module or a dual-line module, determining that the target suspected obstacle area is an obstacle or a low space according to the judgment result includes:

[0119] If the judgment result is that there is a suspected obstacle area within the first preset range of the corresponding area, determining the target suspected obstacle area in the second map as the obstacle location;

[0120] If the judgment result is that there is no suspected obstacle area within the first preset range of the corresponding area, the collision sensor of the cleaning equipment is used to detect the low space, wherein, when the collision sensor is not triggered, the target suspected obstacle area in the second map is determined to be a low space.

[0121] In one embodiment, the structured light module includes a single-line module. In this case, the first map is obtained through the single-line module and can be called a single-line map. The structured light module includes a single-line module, so the front side of the cleaning equipment can be completely covered. When the cleaning equipment enters the second preset range near the grid (cellX_i, cellY_i) of the suspected obstacle judged by the laser radar, the second preset range is, for example, a 10cm range area, and the cleaning equipment will slow down. If the grid (cellX_i, cellY_i) of the suspected obstacle is in the first preset range (for example, 2cm) around the corresponding area in the single-line map, there is a grid with a grid value ≤ the obstacle judgment threshold a×255, that is, there is a suspected obstacle area around the corresponding area in the single-line map, then it is determined that there must be an obstacle that will block the cleaning equipment, and the cleaning equipment can use the obstacle point cloud generated by the laser radar to avoid obstacles.

[0122] If the grid value is greater than or equal to the obstacle determination threshold a×255, it indicates that no obstacle is detected in the forward single line. However, because the measurement height of the single line is typically lower than the fuselage, to avoid misjudgments, obstacle detection will continue to utilize the collision sensor method described in the above embodiment. The collision sensor will probe the forward direction at a low speed. If the collision sensor triggers, it is determined to be a real obstacle. If it does not trigger, it is determined to be a low space. This method improves the accuracy of obstacle detection.

[0123] In addition, in this embodiment, if the second map recognition grid corresponding to the laser radar shows that there is an obstacle, but the first map recognition grid corresponding to the structured light module shows that there is no obstacle, the cleaning device may not be controlled to detect the obstacle by colliding with the collision sensor, but the obstacle persistent map may be directly queried. If the user has marked the grid as 2 in the obstacle persistent map, indicating that there is an obstacle, the cleaning device will perform obstacle avoidance action.

[0124] In another embodiment, the structured light module includes a dual-line module. In this case, the structured light module can detect high objects. The first map is obtained through the dual-line module and can be called a dual-line map. For the grid (cellX_i, cellY_i) of the suspected obstacle judged by the lidar, if the grid (cellX_i, cellY_i) of the suspected obstacle is in the first preset range (for example, 2cm) around the corresponding area in the dual-line map, and there is a grid with a grid value ≤ the obstacle judgment threshold a×255, the grid can be considered to be an actual obstacle. For other grids, obstacle detection is performed by using a collision sensor to detect obstacles. The collision sensor is used to test the front at a low speed. If the collision sensor is triggered, it is determined to be a real obstacle. If the collision sensor is not triggered, it is determined to be a low space. In this way, the accuracy of obstacle detection is improved.

[0125] In addition, in this embodiment, if the second map recognition grid corresponding to the laser radar shows that there is an obstacle, but the first map recognition grid corresponding to the structured light module shows that there is no obstacle, the cleaning device may not be controlled to detect the obstacle by colliding with the collision sensor, but the obstacle persistent map may be directly queried. If the user has marked the grid as 2 in the obstacle persistent map, indicating that there is an obstacle, the cleaning device will perform obstacle avoidance action.

[0126] In other embodiments, for cleaning equipment equipped only with a lifting lidar and no structured light module, when the cleaning equipment enters the 10cm area around a grid (cellX_i, cellY_i) suspected of being an obstacle based on lidar, the cleaning equipment references the persistent obstacle map, slows down, and uses the collision sensor to probe ahead. If the lidar determines that a grid contains an obstacle, the persistent obstacle map is consulted. If the grid is marked as 2, indicating an obstacle, the cleaning equipment performs obstacle avoidance.

[0127] In other embodiments, cleaning equipment equipped with a structured light module and 3D Time of Flight (ToF) can utilize 3D ToF to directly determine the 3D shape of obstacles using a 3D point cloud, enabling obstacle avoidance. Because 3D ToF sensors are typically not mounted on the upper side of a machine, these cleaning equipment do not need to consider the critical height of LiDAR and do not require detection in low areas.

[0128] As a specific implementation of the above cleaning equipment obstacle avoidance method, the embodiment of the present application provides a cleaning equipment obstacle avoidance device, the cleaning equipment includes a structured light module and a laser radar. Figure 4 As shown, the cleaning equipment obstacle avoidance device 400 includes: a data acquisition module 401, a map marking module 402, an obstacle recognition module 403 and an obstacle avoidance control module 404.

[0129] The data acquisition module 401 is configured to acquire first point cloud data through a structured light module and second point cloud data through a laser radar.

[0130] A map marking module 402 is configured to mark a first map corresponding to the structured light module based on the first point cloud data, and mark a second map corresponding to the laser radar based on the second point cloud data;

[0131] The obstacle identification module 403 is used to perform obstacle identification on the first map and the second map to obtain an obstacle identification result;

[0132] The obstacle avoidance control module 404 is used to control the movement of the cleaning device according to the obstacle recognition result.

[0133] Furthermore, the map marking module 402 is used to:

[0134] Establishing a first map corresponding to the structured light module, the first map including a plurality of grids, wherein the numerical value of the grid represents the confidence level that the grid is an obstacle; the first map is a first grid map;

[0135] Determining a first empty area grid and a first obstacle grid in the first map based on the three-dimensional coordinates of the first point cloud data in the three-dimensional space and a preset ground height threshold;

[0136] The first empty area grid is subjected to an empty area filling process, and the first obstacle grid is subjected to an obstacle coloring process, so as to achieve marking of the first map.

[0137] Furthermore, the map marking module 402 is used to:

[0138] Based on the three-dimensional coordinates of the first point cloud data, determine the first point cloud data below the ground height threshold, record it as the first target point cloud data, and determine the first point cloud data above the ground height threshold, record it as the second target point cloud data;

[0139] The grid occupied by the first target point cloud data in the first map is determined to be a first empty area grid, and the grid occupied by the second target point cloud data in the first map is determined to be a first obstacle grid.

[0140] Furthermore, the map marking module 402 is used to:

[0141] Establishing a second map corresponding to the laser radar, the second map including a plurality of grids, wherein the numerical value of the grid represents the confidence level that the grid is an obstacle; the second map is a second grid map;

[0142] Convert the initial coordinates of the second point cloud data acquired by the laser radar into two-dimensional coordinates in two-dimensional space. The initial coordinates of the second point cloud data are marked as (range_i, angle_i, laserPose), where range_i represents the distance of the i-th point cloud from the center of the laser radar, angle_i represents the angle formed by the i-th point cloud and the 0-degree ray of the laser radar, and laserPose represents the pose data of the laser radar.

[0143] Determine a second empty area grid and a second obstacle grid between the target grid occupied by the laser radar and the two-dimensional coordinates;

[0144] The second empty area grid is subjected to an empty area filling process, and the second obstacle grid is subjected to an obstacle coloring process, so as to realize marking of the second map.

[0145] Furthermore, the map marking module 402 is used to:

[0146] The grid between the target grid and the two-dimensional coordinate is used as the second empty area grid, and the grid to which the two-dimensional coordinate belongs is used as the second obstacle grid.

[0147] Furthermore, the empty area grid is subjected to an empty area filling process, including:

[0148] Update the values of the empty area grid according to the empty area elimination formula; the empty area elimination formula is:

[0149] CellValueAfter_i=CellValue_i×ChangeRate_1+(1-ChangeRate_1)×u1

[0150] Among them, CellValueAfter_i is the value of the empty area grid after the update, CellValue_i is the value of the empty area grid before the update, ChangeRate_1 is the preset empty area change rate, and u1 is the upper limit of the grid value;

[0151] Perform obstacle coloring on the obstacle grid, including:

[0152] Update the value of the obstacle grid according to the obstacle coloring formula; the obstacle coloring formula is:

[0153] CellValueAfter_i'=CellValue_i'×ChangeRate_2+(1-ChangeRate_2)×u2

[0154] Among them, CellValueAfter_i' is the value of the obstacle grid after the update, CellValue_i' is the value of the obstacle grid before the update, ChangeRate_2 is the preset obstacle change rate, and u2 is the lower limit of the grid value.

[0155] Furthermore, the obstacle identification module 403 is configured to:

[0156] For at least one of the first map and the second map, a determination is made as to whether there is a suspected obstacle area within a surrounding area with a preset distance as a radius and the position of the cleaning device as the center, and a first obstacle identification result is obtained. The suspected obstacle area is a grid whose value is ≤ a preset threshold value, and the preset threshold value is equal to the product of a preset obstacle determination threshold value and an upper limit value of the grid value.

[0157] Furthermore, the obstacle avoidance control module 404 is further configured to: if the first obstacle identification result indicates that there is a suspected obstacle area in the surrounding area, use the collision sensor of the cleaning device to detect the obstacle; wherein the collision sensor performs obstacle detection by controlling the cleaning device to decelerate and determine a persistent obstacle map in the first map and / or the second map;

[0158] The map marking module 402 is also used to: determine the obstacle position based on the results of obstacle detection; wherein, during the deceleration movement process, when the collision sensor of the cleaning device is triggered, the current position of the cleaning device and the current position of the collision sensor are marked in the obstacle persistent map as the obstacle position.

[0159] Furthermore, the data acquisition module 401 is further configured to: acquire the location of the obstacle marked by the user;

[0160] The map marking module 402 is further configured to update the marked obstacle positions in the persistent obstacle map according to the obstacle positions marked by the user.

[0161] Furthermore, the obstacle identification module 403 is further configured to:

[0162] For the target suspected obstacle area in the second map, determining a corresponding area of the target suspected obstacle area in the first map;

[0163] In the first map, determining whether there is a suspected obstacle area within a first preset range of the corresponding area, and obtaining a determination result;

[0164] According to the judgment result, the target suspected obstacle area is determined to be an obstacle position or a low space;

[0165] The obstacle avoidance control module 404 is specifically used to:

[0166] If the target suspected obstacle area is determined to be the obstacle location, the cleaning equipment is controlled to move to avoid the obstacle location;

[0167] If the target suspected obstacle area is determined to be a low area, the cleaning equipment is controlled to pass through the low area after the laser radar descends.

[0168] Furthermore, if the first map is obtained by a structured light module of a single-line module and a dual-line module, the obstacle recognition module 403 is specifically configured to:

[0169] If the judgment result is that there is a suspected obstacle area within the first preset range of the corresponding area, determining the target suspected obstacle area in the second map as the obstacle location;

[0170] If the judgment result is that there is no suspected obstacle area within the first preset range of the corresponding area, the target suspected obstacle area in the second map is determined to be a low area with a height similar to that of the laser radar in the raised state.

[0171] Furthermore, if the first map is obtained by a structured light module of a single-line module or a dual-line module, the obstacle recognition module 403 is specifically configured to:

[0172] If the judgment result is that there is a suspected obstacle area within the first preset range of the corresponding area, determining the target suspected obstacle area in the second map as the obstacle location;

[0173] If the judgment result is that there is no suspected obstacle area within the first preset range of the corresponding area, the collision sensor of the cleaning equipment is used to detect the low space, wherein, when the collision sensor is not triggered, the target suspected obstacle area in the second map is determined to be a low space.

[0174] The cleaning equipment obstacle avoidance device 400 in the embodiment of the present application can be a cleaning equipment, or a component in the cleaning equipment, such as an integrated circuit or a chip. The cleaning equipment obstacle avoidance device 400 provided in the embodiment of the present application can achieve Figure 1 To avoid repetition, the various processes implemented in the embodiment of the cleaning equipment obstacle avoidance method are not described here.

[0175] The present application also provides a cleaning device, such as Figure 5 As shown, the cleaning device 500 includes a processor 501 and a memory 502. The memory 502 stores programs or instructions that can be run on the processor 501. When the program or instructions are executed by the processor 501, the various steps of the above-mentioned cleaning device obstacle avoidance method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, they will not be repeated here.

[0176] The memory 502 can be used to store software programs and various data. The memory 502 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store an operating system, applications or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory 502 may include volatile memory or non-volatile memory, or the memory 502 may include both volatile and non-volatile memory. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM). The memory 502 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0177] Processor 501 may include one or more processing units. Optionally, processor 501 integrates an application processor and a modem processor. The application processor primarily handles operations related to the operating system, user interface, and application programs, while the modem processor primarily processes wireless communication signals, such as a baseband processor. It is understood that the modem processor may not be integrated into processor 501.

[0178] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the various processes of the above-mentioned cleaning equipment obstacle avoidance method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0179] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be noted that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0180] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

Claims

1. A cleaning equipment obstacle avoidance method, characterized in that: The cleaning device includes a structured light module and a laser radar, and the method includes: Acquire first point cloud data through the structured light module, and acquire second point cloud data through the laser radar; Marking a first map corresponding to the structured light module based on the first point cloud data, and marking a second map corresponding to the laser radar based on the second point cloud data; Obstacle identification is performed on the first map and the second map to obtain an obstacle identification result, and the movement of the cleaning device is controlled according to the obstacle identification result.

2. The method according to claim 1, characterized in that The marking of the first map corresponding to the structured light module based on the first point cloud data includes: Establishing a first map corresponding to the structured light module, wherein the first map includes a plurality of grids, and the values of the grids represent the confidence level that the grids are obstacles; the first map is a first grid map; Determining a first empty area grid and a first obstacle grid in the first map based on the three-dimensional coordinates of the first point cloud data in the three-dimensional space and a preset ground height threshold; The first empty area grid is subjected to an empty area filling process, and the first obstacle grid is subjected to an obstacle coloring process, so as to achieve marking of the first map.

3. The method according to claim 2, characterized in that The determining, based on the three-dimensional coordinates of the first point cloud data in the three-dimensional space and a preset ground height threshold, a first empty area grid and a first obstacle grid in the first map includes: Based on the three-dimensional coordinates of the first point cloud data, determine the first point cloud data below the ground height threshold, record it as the first target point cloud data, and determine the first point cloud data above the ground height threshold, record it as the second target point cloud data; The grid occupied by the first target point cloud data in the first map is determined to be a first empty area grid, and the grid occupied by the second target point cloud data in the first map is determined to be a first obstacle grid.

4. The method according to claim 1, wherein The marking of the second map corresponding to the laser radar based on the second point cloud data includes: Establishing a second map corresponding to the laser radar, wherein the second map includes a plurality of grids, wherein the values of the grids represent the confidence level that the grids are obstacles; the second map is a second grid map; Convert the initial coordinates of the second point cloud data acquired by the laser radar into two-dimensional coordinates in a two-dimensional space. The initial coordinates of the second point cloud data are labeled as (range_i, angle_i, laserPose), where range_i represents the distance of the i-th point cloud from the center of the laser radar, angle_i represents the angle formed by the i-th point cloud and the 0-degree ray of the laser radar, and laserPose represents the pose data of the laser radar. Determining a second empty area grid and a second obstacle grid between the target grid occupied by the laser radar and the two-dimensional coordinates; The second empty area grid is subjected to an empty area filling process, and the second obstacle grid is subjected to an obstacle coloring process, so as to realize marking of the second map.

5. The method according to claim 4, characterized in that Determining a second empty area grid and a second obstacle grid between the target grid occupied by the laser radar and the two-dimensional coordinates includes: The grid between the target grid and the two-dimensional coordinate is used as the second empty area grid, and the grid to which the two-dimensional coordinate belongs is used as the second obstacle grid.

6. The method according to claim 2 or 4, characterized in that Fill in the empty area of the empty area grid, including: Update the value of the empty area grid according to the empty area elimination formula; the empty area elimination formula is: CellValueAfter_i=CellValue_i×ChangeRate_1+(1-ChangeRate_1)×u1 Among them, CellValueAfter_i is the value of the empty area grid after the update, CellValue_i is the value of the empty area grid before the update, ChangeRate_1 is the preset empty area change rate, and u1 is the upper limit of the grid value; Perform obstacle coloring on the obstacle grid, including: Update the value of the obstacle grid according to the obstacle coloring formula; the obstacle coloring formula is: CellValueAfter_i'=CellValue_i'×ChangeRate_2+(1-ChangeRate_2)×u2 Among them, CellValueAfter_i' is the value of the obstacle grid after updating, CellValue_i' is the value of the obstacle grid before updating, ChangeRate_2 is the preset obstacle change rate, and u2 is the lower limit of the grid value.

7. The method according to claim 1, characterized in that The performing obstacle identification on the first map and the second map to obtain an obstacle identification result includes: For at least one of the first map and the second map, a determination is made as to whether there is a suspected obstacle area within a surrounding area with a preset distance as a radius and the position of the cleaning device as the center, and a first obstacle identification result is obtained. The suspected obstacle area is a grid with a value ≤ a preset threshold, and the preset threshold is equal to the product of a preset obstacle determination threshold and an upper limit value of the grid value.

8. An obstacle avoidance device for cleaning equipment, characterized in that: The cleaning equipment includes a structured light module and a laser radar, and the device includes: A data acquisition module, configured to acquire first point cloud data through the structured light module and second point cloud data through the laser radar; a map marking module, configured to mark a first map corresponding to the structured light module based on the first point cloud data, and mark a second map corresponding to the laser radar based on the second point cloud data; an obstacle identification module, configured to perform obstacle identification on the first map and the second map to obtain an obstacle identification result; The obstacle avoidance control module is used to control the movement of the cleaning device according to the obstacle recognition result.

9. A cleaning device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a program or instruction running on the processor, and when the program or instruction is executed by the processor, the steps of the cleaning equipment obstacle avoidance method as described in any one of claims 1 to 7 are implemented.

10. A readable storage medium having a program or instruction stored thereon, characterized in that: When the program or instruction is executed by a processor, the steps of the cleaning equipment obstacle avoidance method according to any one of claims 1 to 7 are implemented.

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