Map display optimization method for cleaning robot

Abstract

The invention discloses a cleaning robot map display optimization method, and belongs to the technical field of robots. The method comprises the following steps: acquiring an environment map of a working environment of the cleaning robot; the method is characterized by further comprising the following steps: screening an obstacle area in the environment map, and filtering the obstacle area from the environment map to obtain a display map; the obstacle area is an area which is closed by an obstacle contour and has an area smaller than a preset area threshold value in the environment map; acquiring a movement track of the cleaning robot in the working environment; judging whether the motion trail enters any obstacle area or not; and under the condition of determining that the motion trail enters any one obstacle area, merging the any one obstacle area into the display map to obtain a new display map. By means of the method, the matching degree of the cleaning track and the room area is effectively improved, and the map is more attractive.

Description

Technical Field

[0001] The present application belongs to the field of robotics technology, and specifically relates to a cleaning robot map display optimization method. Background Art

[0002] In sweeping robot products, in addition to directly operating the robot, the APP has become an indispensable tool, and the UI interface on the APP interacts directly with the customer. Images are the main way for people to obtain information, so the display map that occupies most of the UI interface on the APP has become one of the important factors affecting product quality. A simple and beautiful display map can provide a good user experience and is an important part of improving the quality of sweeping robots.

[0003] The map displayed on the APP includes room outlines, obstacles, and motion track information. The motion track information does not always exist, but changes dynamically during the cleaning process to display the areas that the robot has cleaned and the movement track. A good display map should be one that matches the room outline and the motion track as closely as possible after the cleaning task is completed, minimizes the room areas that the robot cannot reach, and presents a high-definition scanning coverage map to the user on the APP.

[0004] Therefore, filtering out areas that the robot can see but cannot enter and not displaying them in the APP beautified map effectively improves the degree of consistency between the cleaning trajectory and the room area, making the map more beautiful has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present application provides a cleaning robot map display optimization method for solving the following technical problems: how to effectively improve the degree of consistency between the cleaning trajectory and the room area to make the map more beautiful.

[0006] The technical solution adopted in this application is:

[0007] A cleaning robot map display optimization method comprises: obtaining an environment map of the working environment of the cleaning robot; further comprising: screening obstacle areas in the environment map, and filtering the obstacle areas from the environment map to obtain a display map; the obstacle area is an area in the environment map that is closed by an obstacle outline and has an area less than a preset area threshold; obtaining a motion trajectory of the cleaning robot in the working environment; determining whether the motion trajectory enters any obstacle area; and if it is determined that the motion trajectory enters any obstacle area, merging any obstacle area into the display map to obtain a new display map.

[0008] In one implementation of the present application, after obtaining the environment map of the working environment of the cleaning robot, it also includes: obtaining the obstacle contours in the environment map; and performing a morphological closing operation on the obstacle contours through a rectangular kernel.

[0009] In one implementation of the present application, the screening of obstacle areas in the environment map includes: determining a closed area within the environment map based on the obstacle outline, the closed area being an area in the environment map enclosed by the obstacle outline; calculating the area of ​​each closed area; and determining that a closed area whose area is less than a preset area threshold is an obstacle area.

[0010] In one implementation of the present application, after screening and filtering the obstacle areas in the environment map to obtain a display map, it also includes: performing room segmentation on the display map to divide the display map into several room areas; merging any obstacle area into the display map to obtain a new display map, including: determining the nearest room area of ​​any obstacle area; merging any obstacle area into the nearest room area to obtain a new display map.

[0011] In one implementation of the present application, determining the room area closest to any obstacle area includes: determining the minimum bounding rectangle of any obstacle area and each room area; calculating the intersection area of ​​the minimum bounding rectangle of any obstacle area and the minimum bounding rectangle of each room area; and determining the room area closest to any obstacle area based on the intersection area.

[0012] In one implementation of the present application, determining the nearest room area of ​​any obstacle area based on the intersection area includes: when the intersection area is 0, determining that the distance from any obstacle area to the corresponding room is infinite; when the intersection area is not 0, calculating the inverse of the ratio of the intersection area to the area of ​​any obstacle area, and determining the room outline corresponding to the intersection area with the smallest inverse value as the nearest room outline.

[0013] In one implementation of the present application, after obtaining the display map, it also includes: uploading the display map to the APP front-end page for display; and storing the display map and the obstacle area in a disk.

[0014] In an implementation of the present application, after merging any obstacle area into the nearest room outline, the method further includes: updating the display map in the disk, and deleting any obstacle area from the disk.

[0015] In one implementation of the present application, after determining the room outline of each room area, it also includes: determining the room type of each room; and coloring each room area in the display map based on the room type.

[0016] In an implementation of the present application, after merging any obstacle area into the display map, the method further includes: displaying the color of any obstacle area as the color of the most adjacent room area.

[0017] Due to the adoption of the above technical solution, the beneficial effects achieved by this application are as follows:

[0018] 1. Filter out laser scanning areas smaller than a certain area threshold before room segmentation, combine map beautification with obstacle areas, and do not display areas that the machine may not reach, making the map more concise and beautiful.

[0019] 2. During the cleaning process, the robot determines whether the motion trajectory will pass through the obstacle area. If a motion trajectory passes through an obstacle area, the area will be merged into the corresponding room area. The obstacle area that the robot can reach can be screened out and displayed in the beautified map, thereby improving the consistency between the trajectory and the room area.

[0020] 3. The distance is defined by dividing the intersection area of ​​the obstacle area and the minimum circumscribed rectangle of each room by the inverse of the ratio of the obstacle area area. The closer the distance, the more suitable the obstacle is for embedding in the room. The obstacle area can be merged into the most suitable room through the above method.

[0021] 4. By closing the obstacle contour with a 3*3 rectangular kernel, the slightly disconnected obstacle contour can be closed to prevent the obstacle contour from being unclosed due to laser data errors and avoid missing some obstacle areas that the robot may not reach. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] 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:

[0023] Figure 1 A flow chart of a cleaning robot map display optimization method provided in an embodiment of the present application;

[0024] Figure 2 A 3*3 rectangular core provided in an embodiment of the present application;

[0025] Figure 3 A morphological closing operation change diagram provided in an embodiment of the present application;

[0026] Figure 4 A schematic diagram of a filterable obstacle provided in an embodiment of the present application;

[0027] Figure 5 A schematic diagram of a filtered room outline provided in an embodiment of the present application;

[0028] Figure 6 A schematic diagram of a room outline after merging obstacle areas provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to more clearly illustrate the overall concept of the present application, a detailed description is given below in an illustrative manner in conjunction with the accompanying drawings.

[0030] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present application is not limited to the specific embodiments disclosed below.

[0031] In addition, in the description of the present application, it should be understood that the terms "top", "bottom", "inside", "outside", "axial", "radial", "circumferential", etc., indicating orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0032] In this application, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or a communication; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0033] In the present application, unless otherwise clearly specified and limited, the first feature "above" or "below" the second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. In the description of this specification, the description with reference to the terms "implementation method", "example", "one embodiment", "example" or "specific example" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments or examples.

[0034] The present application provides a cleaning robot map display optimization method for solving the following technical problems: how to effectively improve the degree of consistency between the cleaning trajectory and the room area to make the map more beautiful.

[0035] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0036] Figure 1 A flow chart of a cleaning robot map display optimization method provided in an embodiment of the present application.

[0037] The analysis method involved in the embodiments of the present application can be implemented by a terminal device or a server, and the present application does not impose any special restrictions on this. For the convenience of understanding and description, the following embodiments are described in detail by taking a server as an example.

[0038] It should be noted that the server can be a single device or a system composed of multiple devices, that is, a distributed server, and this application does not make any specific limitations on this.

[0039] like Figure 1 As shown, the embodiment of the present application provides a cleaning robot map display optimization method, which specifically includes the following steps:

[0040] Step 101, obtaining an environment map of the working environment of the cleaning robot;

[0041] In one embodiment of the present application, before starting a cleaning task, the cleaning robot first obtains room laser data corresponding to the working environment of the cleaning robot.

[0042] Optionally, obtaining room laser data includes the following steps: obtaining laser point cloud data for constructing a map through a laser ranging sensor; for each laser point data in the laser point cloud data, selecting using a preset window, including the laser point data and the laser point data adjacent to the laser point data; determining the correlation coefficient between the laser point data and its adjacent laser point data; classifying the laser point data according to the size of the correlation coefficient; and filtering out, from the laser point cloud data, the laser point data in the category where the number of laser points is less than a preset threshold.

[0043] Furthermore, in order to avoid the obstacle contour being unclosed due to the laser data error, after obtaining the room laser data corresponding to the working environment of the cleaning robot, the obstacle contour in the environment map can be obtained; and the obstacle contour is processed by morphological closing operation through a rectangular kernel. For example, a 3*3 rectangular kernel is used to perform morphological closing operation on the obstacle contour in the room laser data to obtain an environment map.

[0044] It should be noted that morphological closing is an image processing technique used to fill small holes inside an object or remove small bright details. This is achieved by first performing an erosion operation on the image and then performing a dilation operation. For a 3x3 rectangular kernel, when performing a morphological closing operation, we first need to define a 3x3 rectangular kernel. This can be a kernel consisting of a 3x3 matrix with all values ​​1, such as Figure 2 described. Figure 2 A 3*3 rectangular kernel is provided for an embodiment of the present application. Then slide this kernel on the original image, and for each position, we will calculate the ratio of the pixel value at the center of the kernel to the sum of the values ​​of all elements in the kernel. If this ratio is greater than a preset threshold (for example, 0.5), we consider the pixel to be bright; otherwise, we consider the pixel to be dark. In this way, the corrosion operation can be completed. Next is the expansion operation. The expansion operation is the opposite of the corrosion operation: in the corrosion operation, we remove bright pixels; and in the expansion operation, we add bright pixels. We slide a 3x3 rectangular kernel on the original image, and for each position, if the position is bright and at least one kernel element is bright, we add a pixel at that position. In this way, the morphological closing operation can be completed by the operation of corrosion followed by expansion. This operation can effectively fill small holes inside an object or eliminate small bright details, close slight disconnections in the outline of an obstacle, and avoid missing some obstacle areas that the robot may not reach.

[0045] Figure 3 A morphological closing operation change diagram provided in the embodiment of the present application. Figure 3As shown in the figure, after the morphological closing operation is performed on the obstacle contours in the room laser data through a 3*3 rectangular kernel, the incomplete obstacle contours caused by the laser data errors can be filled.

[0046] Step 102: Screen the obstacle area in the environment map, and filter the obstacle area from the environment map to obtain a display map.

[0047] In one embodiment of the present application, after obtaining an environmental map of the working environment of the cleaning robot, in order to avoid displaying areas that the machine may not reach and make the map more beautiful, obstacle areas in the environmental map are screened and filtered out before room segmentation to obtain a display map.

[0048] It should be noted that the obstacle area in the present application is an area in the environment map that is enclosed by the obstacle outline and whose area is smaller than a preset area threshold.

[0049] In one embodiment of the present application, screening the obstacle area in the environment map specifically includes: screening the obstacle area in the environment map, including: determining the closed area in the environment map according to the obstacle outline, the closed area is the area in the environment map that is closed by the obstacle outline; calculating the area of ​​each closed area; determining the closed area whose area is less than a preset area threshold as the obstacle area. For example, according to the obstacle outline in the environment map, the area that is closed by the obstacle outline on all sides and has an area of ​​less than 2 square meters can be used as the obstacle area.

[0050] Figure 4 A schematic diagram of a filterable obstacle provided in an embodiment of the present application, Figure 5 A schematic diagram of a filtered room outline provided in an embodiment of the present application. Figure 4 As shown in the figure, the obstacle areas in the boxes 401 and 402 are the obstacle areas that can be filtered. The room outline after filtering is as follows: Figure 5 shown.

[0051] Furthermore, after screening and filtering the obstacle areas in the environment map to obtain the display map, the method further includes: performing room segmentation on the display map to divide the display map into a plurality of room areas.

[0052] Furthermore, after determining the room outlines of each room, the method further includes: straightening the room outlines and coloring the room areas on the display map.

[0053] It should be noted that the contour line is made more accurate and smoother by further processing the contour line, such as filtering, smoothing, etc. In the specific implementation process, different algorithms and algorithm combinations may affect the final result, so it is necessary to select and adjust according to the specific situation.

[0054] Optional, including 1. Contour straightening based on geometric features: This method uses the geometric features of the room (such as corners, doors and windows, etc.) to determine the key points on the contour line, and connects these key points to form a straight line or curve. This method is more suitable for situations where the geometric features of the room are more obvious. 2. Contour straightening based on image processing: This method regards the room as an image and uses image processing technology (such as edge detection, binarization, etc.) to determine the position and shape of the contour line. The contour line is then smoothed and corrected to form a straight line or curve. This method is sensitive to the texture and lighting conditions of the room and needs to be adjusted according to actual conditions. 3. Contour straightening based on deep learning: This method uses deep learning models (such as convolutional neural networks) to classify and identify rooms to determine the position and shape of the contour line. This method requires a large amount of training data and computing resources, but can achieve higher accuracy and robustness.

[0055] It should be noted that different methods have different advantages and disadvantages and scope of application, and need to be selected and adjusted according to actual conditions. At the same time, in the process of straightening the contour line, the following points should also be noted:

[0056] 1. Ensure the accuracy and smoothness of the contour line to avoid errors in the mapping results;

[0057] 2. For non-rectangular rooms, you need to consider the shape and characteristics of the room to determine the appropriate outline;

[0058] 3. It is necessary to pay attention to the impact of factors such as obstructions and shadows in the room on the contour line and make corresponding arrangements.

[0059] In one embodiment of the present application, coloring the room area specifically includes: identifying obstacle information of the obstacle area based on the room laser data; determining the room type to which the obstacle area belongs based on a preset obstacle information position mapping table. Coloring the room area based on the room type of each room area. For example, displaying room areas of the same room type in the same color, and distinguishing room areas of different room types in different colors.

[0060] In one embodiment of the present application, after screening and filtering the obstacle areas in the environment map to obtain a display map, the method further includes: uploading the display map to the APP front-end page for display, and storing the display map and the obstacle areas to a disk.

[0061] Step 103: obtaining the motion trajectory of the cleaning robot in the working environment;

[0062] For example, when a cleaning robot cleans a room, the cleaning trajectory of the cleaning robot can be obtained in real time as a motion trajectory.

[0063] Step 104: determine whether the motion trajectory enters any obstacle area;

[0064] Step 105: When it is determined that the motion trajectory enters any obstacle area, any obstacle area is merged into the display map to obtain a new display map.

[0065] In one embodiment of the present application, in the cleaning mode, it is determined in real time whether the motion trajectory will enter the obstacle area. If the trajectory passes through any obstacle area, it means that the robot can enter the obstacle area for cleaning, then the obstacle area is merged into the nearest room area.

[0066] In one embodiment of the present application, any obstacle area is merged into a display map to obtain a new display map, which specifically includes: determining the nearest room area of ​​any obstacle area; merging any obstacle area into the nearest room area to obtain a new display map.

[0067] After determining that the movement trajectory of the cleaning robot in the working environment passes through a certain obstacle area, the obstacle area needs to be displayed in a new map. In order to display the obstacle area in the most appropriate position, the present application needs to determine the room area closest to the location of the obstacle area in the originally displayed map, and merge the obstacle area into the nearest room area for display.

[0068] In one embodiment of the present application, determining the nearest room area of ​​any obstacle area specifically includes: determining the minimum enclosing rectangle of any obstacle area and each room area; calculating the intersection area of ​​the minimum enclosing rectangle of any obstacle area and the minimum enclosing rectangle of each room area; determining the nearest room area of ​​any obstacle area based on the intersection area; merging any obstacle area into the nearest room outline of the obstacle area to obtain a new display map.

[0069] In one embodiment of the present application, the nearest room outline of the obstacle area is determined based on the intersection area, specifically including: when the intersection area is 0, determining that the distance from any obstacle area to the corresponding room is infinite; when the intersection area is not 0, calculating the inverse of the ratio of the intersection area to the area of ​​any obstacle area, and determining the room outline corresponding to the intersection area with the smallest inverse value as the nearest room outline.

[0070] The embodiment of the present application uses the intersection area of ​​the obstacle area and the minimum circumscribed rectangle of each room divided by the inverse of the ratio of the obstacle area area as a distance definition method. The closer the distance, the more suitable the obstacle area is for embedding in the room. The obstacle area can be merged into the most suitable room through the above method.

[0071] Figure 6 This is a schematic diagram of a room outline after the obstacle area is merged provided in an embodiment of the present application. Figure 6 As shown, the bottommost obstacle area is merged into the corresponding nearest neighbor room.

[0072] In one embodiment of the present application, after any obstacle area is merged into the display map, the method further includes: displaying the color of any obstacle area as the color of the most adjacent room area.

[0073] In one embodiment of the present application, after merging the obstacle area into the nearest room outline, the method further includes: updating the display map in the disk and deleting any obstacle area from the disk.

[0074] Those skilled in the art can understand that if there is a cleaning robot, the map displayed after the map is constructed is not completely consistent with the map finally displayed after the environment is cleaned. For example, after the map is constructed, there is a small area of ​​obstacle area that is not displayed in the map, or even during the cleaning process, the cleaning track passes through the displayed map and enters the small area that is not displayed, and after the cleaning is completed, the small area passed by the cleaning track is displayed in the new map, then it can be considered that the cleaning robot has applied the solution disclosed in this application, and it can be considered to be infringing.

[0075] Anything not described in this application can be achieved by adopting or drawing on existing technologies.

[0076] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0077] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0078] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0079] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0080] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0081] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0082] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0083] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0084] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0085] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0086] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A cleaning robot map display optimization method, the method comprising: Obtain an environmental map of the working environment of the cleaning robot; Characterized in that the method further comprises: Screening an obstacle area in the environment map, and filtering the obstacle area from the environment map to obtain a display map; the obstacle area is an area in the environment map that is enclosed by an obstacle outline and has an area smaller than a preset area threshold; Obtaining a motion trajectory of the cleaning robot in the working environment; Determining whether the motion trajectory enters any obstacle area; When it is determined that the motion trajectory enters any obstacle area, any obstacle area is merged into the display map to obtain a new display map.

2. A cleaning robot map display optimization method according to claim 1, characterized in that: After obtaining the environment map of the working environment of the cleaning robot, the method further includes: Obtaining obstacle contours in the environment map; The obstacle contour is processed by a morphological closing operation using a rectangular kernel.

3. A cleaning robot map display optimization method according to claim 2, characterized in that: The screening of the obstacle area in the environment map comprises: Determine a closed area in the environment map according to the obstacle contour, wherein the closed area is an area in the environment map that is closed by the obstacle contour; Calculate the area of ​​each enclosed region; A closed area whose area is smaller than a preset area threshold is determined as an obstacle area.

4. A cleaning robot map display optimization method according to claim 1, characterized in that: After screening and filtering the obstacle area in the environment map to obtain a display map, the method further includes: Performing room segmentation on the display map to divide the display map into a plurality of room areas; The step of merging any obstacle area into the display map to obtain a new display map includes: Determine the room area closest to any obstacle area; Merge any obstacle area into the nearest room area to obtain a new display map.

5. A cleaning robot map display optimization method according to claim 4, characterized in that: The determining of the room area that is closest to any obstacle area includes: Determine the minimum bounding rectangle of any obstacle area and each room area; Calculate the intersection area of ​​the minimum bounding rectangle of any obstacle area and the minimum bounding rectangle of each room area; The most adjacent room area of ​​any obstacle area is determined based on the intersection area.

6. A cleaning robot map display optimization method according to claim 5, characterized in that: The determining the nearest room area of ​​any obstacle area based on the intersection area includes: When the intersection area is 0, determining that the distance from any obstacle area to the corresponding room is infinite; When the intersection area is not 0, the inverse of the ratio of the intersection area to the area of ​​any obstacle region is calculated, and the room outline corresponding to the intersection area with the smallest inverse value is determined as the most adjacent room outline.

7. A cleaning robot map display optimization method according to claim 4, characterized in that: After obtaining the display map, the method further includes: Upload the displayed map to the APP front-end page for display; The display map and the obstacle area are stored in a disk.

8. A cleaning robot map display optimization method according to claim 7, characterized in that: After merging any obstacle area into the nearest room outline, the method further includes: The display map in the disk is updated, and any obstacle area is deleted from the disk.

9. A cleaning robot map display optimization method according to claim 4, characterized in that: After determining the room outlines of each room area, the method further comprises: Determine the room type for each room; Based on the room type, each room area in the display map is colored.

10. A cleaning robot map display optimization method according to claim 9, characterized in that: After merging any obstacle area into the display map, the method further includes: The color of any obstacle area is displayed as the color of the most adjacent room area.

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