A method for segmenting working scene maps of cleaning robots

Abstract

The present disclosure provides a method for segmenting a working scene map region of a cleaning robot, comprising: obtaining a global two-dimensional map of the working scene of the cleaning robot; performing expansion processing on each pixel of the global two-dimensional map by an image pixel expansion method to obtain an expanded global two-dimensional map; detecting line segments in the expanded global two-dimensional map by a random consistency sampling algorithm; dividing all line segments and discrete pixel points in the map into unreachable areas, and dividing areas outside the line segments and discrete pixel points into reachable areas; and dividing the reachable areas into different connected domains. The present disclosure also provides a device for segmenting a working scene map region of a cleaning robot, an electronic device, and a readable storage medium.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent control of cleaning robots, and in particular to a method for segmenting a working scene map area of ​​a cleaning robot. Background Art

[0002] At present, cleaning robots, such as household sweeping robots, commercial sweeping robots, commercial washing and mopping robots, commercial floor polishing robots and commercial carpet cleaning robots, have been widely used in cleaning work in scenes such as homes, hotels, conference centers, supermarkets and exhibition centers. If the entire large work scene is cleaned as a whole, it will be time-consuming and inconsistent with the conventional cleaning logic. Moreover, if the one-time cleaning area is too large, the scene is often more dynamic, and the cleaning robot is more susceptible to interference, which affects the cleaning efficiency and effect. At the same time, considering the endurance of the cleaning robot, if the cleaning area is too large, the robot may need to be charged multiple times to complete the cleaning, which is very time-consuming, and the final cleaning efficiency is low, the cleaning effect is also poor, and the robot's cleaning trajectory cannot intuitively show the visual effect of its cleaning task. Summary of the invention

[0003] In order to solve at least one of the above technical problems, the present disclosure provides a method and device for segmenting a working scene map area of ​​a cleaning robot.

[0004] According to one aspect of the present disclosure, a method for segmenting a working scene map area of ​​a cleaning robot is provided, comprising:

[0005] S1, obtaining a global two-dimensional map of the working scene of the cleaning robot;

[0006] S2, dilating each pixel of the global two-dimensional map by an image pixel dilation method to obtain an expanded global two-dimensional map;

[0007] S3, detecting line segments in the expanded global two-dimensional map by a random consistency sampling algorithm;

[0008] S4, dividing all line segments and discrete pixel points in the map into unreachable areas, and dividing areas outside the line segments and discrete pixel points into reachable areas;

[0009] S5. Divide the reachable area into different connected domains, and use the connected domains as a working map of the cleaning robot.

[0010] According to at least one embodiment of the present disclosure, a method for segmenting a working scene map area of ​​a cleaning robot is provided to obtain a global two-dimensional map of a working scene of the cleaning robot, including:

[0011] The cleaning robot scans the two-dimensional distance information of a certain height of the working scene through a laser sensor, continuously obtains laser data of each time frame, and uses the global two-dimensional grid map of the cleaning robot's working scene constructed by the SLAM method as the global two-dimensional map.

[0012] According to the method for segmenting the working scene map area of ​​a cleaning robot in at least one embodiment of the present disclosure, S3 further includes:

[0013] The distance between the adjacent line segments is calculated, and if the distance between the adjacent line segments is less than a preset distance threshold, the adjacent line segments are connected into one line segment.

[0014] According to at least one embodiment of the present disclosure, a method for segmenting a working scene map area of ​​a cleaning robot is provided, wherein the reachable area is divided into different connected domains, including:

[0015] Based on each pixel point in the reachable area, a connected domain is found for each pixel point through a breadth-first search algorithm, and finally the reachable area is divided into different connected domains.

[0016] According to at least one embodiment of the present disclosure, the method for segmenting the working scene map area of ​​a cleaning robot further includes, after S5:

[0017] The area of ​​each connected domain is calculated, and if the area of ​​the connected domain is greater than a first preset area threshold, the corresponding connected domain is segmented at least once, so that the area of ​​the segmented connected domain is less than the first preset area threshold.

[0018] According to at least one embodiment of the present disclosure, the method for segmenting the working scene map area of ​​a cleaning robot further includes, after S5:

[0019] The area of ​​each connected domain is calculated. If the area of ​​the connected domain is less than a second preset area threshold, a small-area connected domain is formed. Then, through pixel sharpening processing, the small-area connected domain is merged into a connected domain adjacent to the small-area connected domain to eliminate the small-area connected domain.

[0020] According to at least one embodiment of the present disclosure, the method for segmenting the working scene map area of ​​a cleaning robot further includes, after S5:

[0021] The connected domains are marked, and different connected domains have different marking symbols.

[0022] According to another aspect of the present disclosure, a working scene map area segmentation device for a cleaning robot is provided, comprising:

[0023] A global two-dimensional map acquisition module is used to acquire a global two-dimensional map of the working scene of the cleaning robot;

[0024] An expansion processing module, which performs expansion processing on each pixel of the global two-dimensional map by an image pixel expansion method to obtain an expanded global two-dimensional map;

[0025] A line segment detection module, which detects line segments in the expanded global two-dimensional map through a random consistency sampling algorithm;

[0026] A region division module divides all line segments and discrete pixel points in the map into unreachable areas, and divides areas outside the line segments and discrete pixel points into reachable areas;

[0027] The connected domain division and marking module divides the reachable area into different connected domains and marks the connected domains.

[0028] According to one aspect of the present disclosure, there is provided an electronic device, including:

[0029] a memory storing execution instructions; and

[0030] A processor, wherein the processor executes the execution instruction stored in the memory, so that the processor executes any one of the methods described above.

[0031] According to another aspect of the present disclosure, a readable storage medium is provided, wherein the readable storage medium stores execution instructions, and the execution instructions are used to implement any of the above-mentioned methods when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings illustrate exemplary embodiments of the present disclosure and together with the description serve to explain the principles of the present disclosure. These drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification.

[0033] Figure 1 It is a flowchart of a method for segmenting a working scene map area of ​​a cleaning robot according to an embodiment of the present disclosure.

[0034] Figure 2 It is a schematic diagram of an original global two-dimensional map of a working scene of a cleaning robot according to an embodiment of the present disclosure.

[0035] Figure 3 It is a schematic diagram of a global two-dimensional map of a working scene of a cleaning robot after an expansion algorithm is performed according to an embodiment of the present disclosure.

[0036] Figure 4 It is a schematic diagram of a global two-dimensional map of a cleaning robot connecting adjacent line segments according to an embodiment of the present disclosure.

[0037] Figure 5 It is a schematic diagram of a global two-dimensional map of a cleaning robot that performs breadth-first search for connected domains and assigns different labels according to an embodiment of the present disclosure.

[0038] Figure 6 It is a schematic diagram of a global two-dimensional map of a cleaning robot that divides a large connected domain according to an embodiment of the present disclosure.

[0039] Figure 7 It is a schematic diagram of a global two-dimensional map of a cleaning robot after map pixels are sharpened according to an embodiment of the present disclosure.

[0040] Figure 8 It is a schematic diagram of a global two-dimensional map of a cleaning robot after eliminating small connected domains according to an embodiment of the present disclosure.

[0041] Fig. 9 This is a schematic diagram of the structure of a working scene map area segmentation device for a cleaning robot according to an embodiment of the present disclosure.

[0042] Description of Reference Numerals

[0043] 1000 Cleaning robot's work scene map area segmentation device

[0044] 1002 Global 2D map acquisition module

[0045] 1004 Expansion Processing Module

[0046] 1006 Line segment detection module

[0047] 1008 Area Division Module

[0048] 1010 Connected domain division and labeling module

[0049] 1100 Bus

[0050] 1200 processor

[0051] 1300 Memory

[0052] 1400 Other circuits. DETAILED DESCRIPTION

[0053] The present disclosure is further described in detail below in conjunction with the accompanying drawings and implementations. It is understood that the specific implementations described herein are only used to explain the relevant content, rather than to limit the present disclosure. It should also be noted that, for ease of description, only the parts related to the present disclosure are shown in the accompanying drawings.

[0054] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in the present disclosure can be combined with each other. The technical solution of the present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0055] Unless otherwise specified, the exemplary embodiments / embodiments shown will be understood as providing exemplary features of various details of some ways in which the technical concept of the present disclosure can be implemented in practice. Therefore, unless otherwise specified, the features of the various embodiments / embodiments can be combined, separated, interchanged and / or rearranged without departing from the technical concept of the present disclosure.

[0056] The use of cross-hatching and / or shading in the accompanying drawings is generally used to make the boundaries between adjacent components clear. As such, unless otherwise specified, the presence or absence of cross-hatching or shading does not convey or indicate any preference or requirement for the specific materials, material properties, dimensions, proportions, commonalities between the components shown, and / or any other characteristics, attributes, properties, etc. of the components. In addition, in the accompanying drawings, the sizes and relative sizes of the components may be exaggerated for clarity and / or descriptive purposes. When the exemplary embodiments can be implemented differently, the specific process sequence can be performed in a different order than described. For example, two successively described processes can be performed substantially simultaneously or in an order opposite to the described order. In addition, the same figure numbers represent the same components.

[0057] When a component is referred to as being "on" or "over," "connected to," or "coupled to" another component, the component may be directly on, directly connected to, or directly coupled to the other component, or intervening components may be present. However, when a component is referred to as being "directly on," "directly connected to," or "directly coupled to" another component, there are no intervening components. For this purpose, the term "connected" may refer to a physical connection, an electrical connection, etc., with or without intervening components.

[0058] The terms used herein are for the purpose of describing specific embodiments, and are not intended to be restrictive. As used herein, unless the context clearly indicates otherwise, the singular forms "one (kind, person)" and "said (the)" are also intended to include plural forms. In addition, when the terms "comprise" and / or "include" and their variations are used in this specification, it is explained that there are stated features, integral bodies, steps, operations, parts, assemblies and / or their groups, but it is not excluded that there are or add one or more other features, integral bodies, steps, operations, parts, assemblies and / or their groups. It should also be noted that, as used herein, the terms "substantially", "approximately" and other similar terms are used as approximate terms and not as degree terms, so that they are used to explain the inherent deviations of the measured values, calculated values ​​and / or the values ​​provided that will be recognized by those of ordinary skill in the art.

[0059] To segment the area to be cleaned, firstly, the physical size, passability, turning radius and other parameters of the cleaning robot are used to set the minimum and maximum area of ​​each sub-area to be cleaned. At the same time, the complexity of the actual working scene should be taken into account, including relatively closed areas formed by independent rooms, tables and wall pillars. Therefore, from the perspective of cleaning logic, it can be used as a one-time cleaning object area, and these relatively closed areas cannot be connected. It is of great significance to quickly and effectively segment the area to be cleaned by the cleaning robot.

[0060] Figure 1 It is a flowchart of a method for segmenting a working scene map area of ​​a cleaning robot according to an embodiment of the present disclosure.

[0061] like Figure 1 As shown, the working scene map area segmentation method S100 of the cleaning robot includes:

[0062] S102, obtaining a global two-dimensional map of the working scene of the cleaning robot;

[0063] S104, performing expansion processing on each pixel of the global two-dimensional map by an image pixel expansion method to obtain an expanded global two-dimensional map;

[0064] S106, detecting line segments in the expanded global two-dimensional map by using a random consistency sampling algorithm;

[0065] S108, dividing all line segments and discrete pixel points in the map into unreachable areas, and dividing areas outside the line segments and discrete pixel points into reachable areas;

[0066] S110, dividing the reachable area into different connected domains, and using the connected domains as a working map of the cleaning robot.

[0067] Among them, in the above step S102, a global two-dimensional map of the working scene of the cleaning robot is obtained, including: the cleaning robot scans the two-dimensional distance information of the working scene at a certain height through a laser sensor, continuously obtains laser data in each time frame, and uses the global two-dimensional grid map of the working scene of the cleaning robot constructed by the SLAM method as the global two-dimensional map.

[0068] The cleaning robot is responsible for tasks such as sweeping, washing, and polishing the ground in the work scene. In order to ensure cleaning efficiency, the cleaning robot needs to pre-build a global two-dimensional map of the work scene, so that it can plan the corresponding cleaning route based on the constructed global two-dimensional map and effectively and sequentially clean the area to be cleaned. The cleaning robot mainly perceives the environmental information of the work scene through various on-board sensors, such as two-dimensional laser, three-dimensional laser, and vision sensors, and constructs a global two-dimensional or three-dimensional map of the work scene through the SLAM method. The cleaning robot uses a laser sensor to scan the two-dimensional distance information of the work scene at a certain height, and continuously obtains laser data in each time frame to construct a global two-dimensional grid map of the work scene. , which is characterized by , Represents the pixel points in the two-dimensional map of the working scene The pixel value of is the horizontal and vertical coordinate set of the two-dimensional map pixel points. The global two-dimensional map of the work scene can represent the work scene as a certain number of square small grids horizontally and vertically, that is, map pixels.

[0069] The global two-dimensional map constructed by the cleaning robot has two-dimensional map pixels, which are small square grids. Depending on whether the cleaning robot can pass or clean in this small grid area, it can be divided into cleanable areas and uncleanable areas. The cleanable area is the area where the robot can travel, also known as the obstacle-free area, and the pixel value of the corresponding pixel is represented by the number 0; the uncleanable area is the area that the robot cannot reach, such as walls, pillars, etc., also known as the obstacle area, and the pixel value of the corresponding pixel is represented by the number 1; of course, some restricted areas and forbidden lines can also be set artificially in the global map to prevent the cleaning robot from passing through this area, turning the obstacle-free area into an obstacle area, so that the robot will no longer clean this part of the area. Considering that the scanning perception range of the laser sensor is limited and has a certain perception angle, the pixel value of the working scene area that cannot be scanned is represented by -1, that is, the undetermined area, and its expression is:

[0070]

[0071] The cleaning robot has built a global two-dimensional grid map of the working scene Schematic diagram as Figure 2As shown, it can be seen as a black and white two-dimensional image composed of small grids. Considering the size of the image display, the grids are very small and can be seen as pixels.

[0072] For the above step S104, each pixel of the global two-dimensional map is expanded by using an image pixel expansion method to obtain an expanded global two-dimensional map. Figure 3 It is a schematic diagram of an expanded global two-dimensional map according to a disclosed embodiment.

[0073] like Figure 3 As shown, based on Figure 2 Global 2D grid map of Global two-dimensional map after dilation algorithm processing Specifically, the expansion process of the global two-dimensional map is as follows.

[0074] After the cleaning robot builds a global two-dimensional grid map of the working scene, it can obtain the value of each pixel in the map. Considering that the pixels with a pixel value of 1 in the global two-dimensional map may be discrete isolated points or point clusters, and the distance between points or point clusters is less than the space required for the cleaning robot to pass safely, in order to save subsequent calculations, the map pixel expansion method is applied to expand each pixel of the global map so that more pixels are considered to be on the same line. The map pixel expansion method mainly connects two or more closely spaced points or point clusters together, which is a very effective means to fill the holes in the image with obstacles. The image after expansion is represented as follows: :

[0075]

[0076] By dragging structural elements On the global 2D map The image pixel domain moves, at each pixel position, when the structural element The center point is translated to A point on the image If the pixel of the structural element intersects with at least one pixel of the obstacle point, keep Pixel points, to achieve the effect of expanding the boundary outward. That is, traversing the target image For the target area in , only the grayscale values ​​of the target pixel and its adjacent upper, lower, left, and right pixels are considered to confirm whether they have intersections with the dilated structural element, that is, there is at least one corresponding grayscale value that is equal. If there is an intersection, the pixel is processed, otherwise the pixel is deleted.

[0077] For the above step S106, line segments are detected in the expanded global two-dimensional map by using a random consistency sampling algorithm. Figure 4 As shown, in Figure 3 Global 2D map after map expansion Schematic diagram of the effect of detecting line segments. Figure 4 A random consistency sampling algorithm is used to find the corresponding line segments and connect the adjacent line segments. The specific processing process is as follows.

[0078] First, the global two-dimensional map of the working scene after expansion , characterized by ,in, Represents the pixel points in the two-dimensional map of the working scene The pixel value of is the horizontal and vertical coordinate set of the pixel points in the two-dimensional map. Considering that the regional segmentation of the two-dimensional map is mainly realized by relying on the line segment features in the scene, that is, according to the lines or line segments in the map, generally referring to straight line segments, the two-dimensional map is divided into different sub-map areas. Relatively speaking, these sub-map areas have a certain degree of independence or closure, such as small rooms in the work scene, or small planned areas separated by tables and chairs. Considering that the actual original observation data of the laser sensor is discrete and has certain measurement errors, the lines or line segments are not absolutely straight lines, nor are they continuous. Even after the above expansion algorithm, there are still discrete noise points.

[0079] Secondly, the random consensus sampling algorithm (RANSAC algorithm) is used to find the pixel points on the line segment model in a series of pixel points in the two-dimensional image according to the input line segment model. The specific input model selected is the straight line formula ,in and That is, the horizontal and vertical coordinates of the pixel points in the above two-dimensional map image. and is the linear model parameter. The input data is the global two-dimensional map of the working scene All pixels in , put all the pixels in the map into this straight line model to calculate the error, find all the points that meet the error threshold, and repeat the error calculation process until a certain number of iterations are reached, then select the model with the most support as the solution to the problem, and output it as all the found line segments. Further, filter out the shorter line segments among the found line segments , is the line segment symbol, Indicates the sequence number. Considering that there may be different numbers of line segments in the map, It can be any natural number greater than 0.

[0080] Optionally, the above step S106 further includes: for the detected line segments, calculating the distance between adjacent line segments, if the distance between adjacent line segments is less than a preset distance threshold, connecting the adjacent line segments into one line segment. Specifically, a distance threshold is set, and when the distance between adjacent line segments is less than the distance threshold, all adjacent line segments are connected, and the processing formula for connecting line segments is as follows:

[0081]

[0082] in, and For adjacent serial numbers and The line segment, Represents a line segment and The distance between The line segment distance threshold is set in advance. Less than When and Connect, that is, generate a new line segment , that is, to form and The pixel point sets are combined to generate a new point set to represent the line segment .

[0083] For the above step S108, all line segments and discrete pixel points in the global two-dimensional map are divided into unreachable areas, and areas outside the line segments and discrete pixel points are divided into reachable areas. Figure 5 It is a schematic diagram of the division result of the reachable area and the unreachable area according to an embodiment of the present disclosure.

[0084] like Figure 5 As shown, Figure 4 A schematic diagram of the global two-dimensional map after all line segments and pixel points in the image are classified as unreachable areas and other areas are reachable areas. The specific processing process is as follows.

[0085] Traverse the global 2D map of the work scene All pixels in , the two-dimensional map All line segments and pixels in the image are classified as unreachable areas. , Unreachable area The other areas are reachable areas. , Reachable area Serial number.

[0086] Global 2D map based on the dilation algorithm The pixel value is updated, 1 represents the pixel value of the unreachable area, and 0 represents the pixel value of the reachable area. The calculation formula is as follows:

[0087]

[0088] Unreachable area The pixel value of all pixels in the reachable area is 1. The pixel value of all pixels in is 0. Figure 4 The global two-dimensional map diagram after all line segments and pixel points are classified as unreachable areas and other areas are reachable areas is as follows Figure 5 shown.

[0089] The above step S110, dividing the reachable area into different connected domains, includes: based on each pixel point in the reachable area, finding a connected domain for each pixel point through a breadth-first search algorithm, and finally dividing the reachable area into different connected domains. Optionally, after the above step S110, it also includes: marking the connected domains, and different connected domains have different marking symbols. Figure 6 It is a schematic diagram of the effect of dividing a reachable area into different connected domains and marking the connected domains according to an embodiment of the present disclosure.

[0090] like Figure 6 As shown, Figure 5 A schematic diagram of a global two-dimensional map is formed by using a breadth-first search algorithm to find a connected domain for each point and assigning the same label to each point in the connected domain. The processing process is as follows.

[0091] On the global 2D map The accessible area In the , the breadth-first search algorithm is used to find the connected domain for each pixel in the reachable area, and the same label is assigned to each point in the connected domain. Breadth-first traversal is a traversal strategy for connected graphs. Its idea is to start from a reachable area. Starting from a certain pixel point, the wider area around it is traversed radially, mainly The pixel value of all the surrounding points , the traversal ends and a connected domain is obtained , For this connected domain The label number, is a natural number greater than 0. For different connected domains , its label number different.

[0092] Optionally, after S110, the method further includes: calculating the area of ​​each connected domain, and if the area of ​​the connected domain is greater than a first preset area threshold, segmenting the corresponding connected domain at least once so that the area of ​​the segmented connected domain is less than the first preset area threshold. When the cleaning area is larger than the maximum cleaning area of ​​the sub-region (i.e., the first preset area threshold), it needs to be segmented twice to be segmented into sub-connected domains smaller than the maximum cleaning area of ​​the sub-region. The specific processing process includes: first, check each connected domain If the maximum size is larger than the maximum cleaning area threshold, , it is divided into two different connected domains until the maximum size of all connected domains is less than the threshold .

[0093] Optionally, after obtaining a global 2D map Each connected domain Afterwards, the global map is sharpened. Figure 7 It is a schematic diagram of a global two-dimensional map sharpening processing effect according to an embodiment of the present disclosure.

[0094] like Figure 7 As shown, Figure 6 Schematic diagram of the global two-dimensional map after map pixel sharpening. Considering that the connected domain is closely connected to the inaccessible area, the edges and grayscale jumps of the image are enhanced to make the image clear and compensate for the image contour. Image sharpening is applied to the global map to highlight the edges, contours, or features of certain linear target elements of the objects on the image. The pixel sharpening method mainly improves the contrast between the edges of the connected domain and the pixels in the inaccessible area.

[0095] Optionally, after S110, the process further includes: calculating the area of ​​each connected domain, and if a small-area connected domain is formed when the area of ​​the connected domain is less than a second preset area threshold, the small-area connected domain is merged into a connected domain adjacent to the small-area connected domain through pixel sharpening processing to eliminate the small-area connected domain. Some isolated noise points can also be eliminated through sharpening. Figure 8 It is a schematic diagram of a global two-dimensional map after eliminating small-area connected domains according to an embodiment of the present disclosure.

[0096] like Figure 8 As shown, eliminate Figure 7 Schematic diagram of the global two-dimensional map after merging the smaller connected domains into the adjacent connected domains. The processing process is as follows.

[0097] After sharpening the pixels of the global two-dimensional map, a new global two-dimensional map is generated , re-traverse the global two-dimensional map The pixels and connected domains in the middle image are considered. For the connected domains with smaller areas, the cleaning robot will clean the connected domains smaller than the minimum cleaning area threshold in order to improve the cleaning efficiency. The smaller connected domains are merged into the adjacent connected domains, thus completing the segmentation of the global map area.

[0098] It should be understood that although this specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each implementation mode may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

[0099] The series of detailed descriptions listed above are only specific descriptions of feasible implementation methods of the present invention. They are not intended to limit the scope of protection of the present invention. Any equivalent implementation methods or changes that do not deviate from the technical spirit of the present invention should be included in the scope of protection of the present invention.

[0100] The present disclosure provides a method for segmenting the working scene map area of ​​a cleaning robot. First, a global map of the working scene to be cleaned is constructed, and based on this map, the area to be cleaned is segmented to generate a series of small sub-areas, and then these small sub-areas are cleaned in sequence. By segmenting the global maps of the working scenes to be cleaned, it is possible to plan the entire scene in a logical and sequential manner, which can greatly improve the cleaning efficiency and the cleaning effect. The method for segmenting the working scene map area of ​​a cleaning robot provided by the present disclosure can meet the cleaning robot's requirements for the global two-dimensional map area segmentation of the working scene, improve the robot's cleaning efficiency, and can also effectively ensure the consistency of the visual effect of each cleaning trajectory.

[0101] According to another aspect of the present disclosure, a working scene map area segmentation device 1000 of a cleaning robot is provided, comprising:

[0102] A global two-dimensional map acquisition module 1002 is used to acquire a global two-dimensional map of the working scene of the cleaning robot;

[0103] An expansion processing module 1004 performs expansion processing on each pixel of the global two-dimensional map by using an image pixel expansion method to obtain an expanded global two-dimensional map;

[0104] The line segment detection module 1006 detects line segments in the expanded global two-dimensional map through a random consistency sampling algorithm;

[0105] The area division module 1008 divides all line segments and discrete pixel points in the map into unreachable areas, and divides the areas outside the line segments and discrete pixel points into reachable areas;

[0106] The connected domain division and marking module 1010 divides the reachable area into different connected domains and marks the connected domains.

[0107] It should be noted that the implementation details of each module in this embodiment are consistent with the implementation method of the working scene map area segmentation method of the cleaning robot provided in the present disclosure, and will not be repeated here.

[0108] According to one aspect of the present disclosure, there is provided an electronic device, including:

[0109] A memory, the memory stores execution instructions;

[0110] The processor executes the execution instructions stored in the memory, so that the processor executes any of the above methods.

[0111] According to another aspect of the present disclosure, a readable storage medium is provided, in which execution instructions are stored. When the execution instructions are executed by a processor, they are used to implement any of the above methods.

[0112] Fig. 9 An example diagram of an apparatus using a hardware implementation of a processing system is shown. The apparatus may include a corresponding module that performs each or several steps in the above flowchart. Therefore, each step or several steps in the above flowchart may be performed by a corresponding module, and the apparatus may include one or more of these modules. The module may be one or more hardware modules specifically configured to perform the corresponding steps, or implemented by a processor configured to perform the corresponding steps, or stored in a computer-readable medium for implementation by a processor, or implemented by some combination.

[0113] The hardware structure can be implemented using a bus architecture. The bus architecture can include any number of interconnecting buses and bridges, depending on the specific application and overall design constraints of the hardware. The bus 1100 connects various circuits including one or more processors 1200, memory 1300, and / or hardware modules together. The bus 1100 can also connect various other circuits 1400 such as peripherals, voltage regulators, power management circuits, external antennas, etc.

[0114] The bus 1100 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Component (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the figure only uses one connecting line, but does not mean that there is only one bus or one type of bus.

[0115] Any process or method description in the flowchart or otherwise described herein can be understood as a module, fragment or portion of a code representing one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present disclosure includes other implementations, in which the functions may not be performed in the order shown or discussed, including performing the functions in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by a person skilled in the art of the present disclosure. The processor performs the various methods and processes described above. For example, the method implementation in the present disclosure can be implemented as a software program, which is tangibly contained in a machine-readable medium, such as a memory. In some embodiments, part or all of the software program can be loaded and / or installed via a memory and / or a communication interface. When the software program is loaded into the memory and executed by the processor, one or more steps in the method described above can be performed. Alternatively, in other embodiments, the processor can be configured to perform one of the above methods in any other appropriate manner (e.g., by means of firmware).

[0116] The logic and / or steps represented in the flowchart or otherwise described herein may be embodied in any readable storage medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch instructions from and execute instructions on an instruction execution system, apparatus, or device), or in combination with such instruction execution systems, apparatuses, or devices.

[0117] For the purposes of this specification, a "readable storage medium" may be any device that can contain, store, communicate, propagate or transmit a program for use with or in conjunction with an instruction execution system, device or apparatus. More specific examples of readable storage media (a non-exhaustive list) include the following: an electrical connection with one or more wirings (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), a fiber optic device, and a portable read-only memory (CDROM). In addition, the readable storage medium may even be paper or other suitable medium on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a memory.

[0118] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0119] A person skilled in the art may understand that all or part of the steps of implementing the above-mentioned implementation method may be completed by instructing related hardware through a program, and the program may be stored in a readable storage medium, which, when executed, includes one or a combination of the steps of the implementation method.

[0120] In addition, each functional unit in each embodiment of the present disclosure may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a readable storage medium. The storage medium may be a read-only memory, a disk or an optical disk, etc.

[0121] In the description of this specification, the description with reference to the terms "one embodiment / method", "some embodiments / methods", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment / method or example are included in at least one embodiment / method or example of the present application. In this specification, the schematic representations of the above terms are not necessarily the same embodiment / method or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments / methods or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments / methods or examples described in this specification and the features of the different embodiments / methods or examples, unless they are contradictory.

[0122] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0123] Those skilled in the art should understand that the above embodiments are only for the purpose of clearly illustrating the present disclosure, and are not intended to limit the scope of the present disclosure. For those skilled in the art, other changes or modifications may be made based on the above disclosure, and these changes or modifications are still within the scope of the present disclosure.

Claims

1. A method for segmenting the working scene map area of ​​a cleaning robot. It is characterized in that include: S1, obtaining a global two-dimensional map of the working scene of the cleaning robot; S2, dilating each pixel of the global two-dimensional map by an image pixel dilation method to obtain an expanded global two-dimensional map; S3, detecting line segments in the expanded global two-dimensional map by a random consistency sampling algorithm; S4, dividing all line segments and discrete pixel points in the global two-dimensional map into unreachable areas, and dividing areas outside the line segments and discrete pixel points into reachable areas; S5, dividing the reachable area into different connected domains, and using the connected domains as a working map of the cleaning robot; S6, calculating the area of ​​each connected domain, if the area of ​​the connected domain is greater than a first preset area threshold, segmenting the corresponding connected domain at least once, so that the area of ​​the segmented connected domain is less than the first preset area threshold; if a small-area connected domain is formed when the area of ​​the connected domain is less than the second preset area threshold, the small-area connected domain is merged into a connected domain adjacent to the small-area connected domain through pixel sharpening processing to eliminate the small-area connected domain; The step of dividing the reachable area into different connected domains includes: based on each pixel point in the reachable area, searching for a connected domain for each pixel point by a breadth-first search algorithm, and finally dividing the reachable area into different connected domains; Wherein, the S3 further includes: calculating the distance between the adjacent line segments, and if the distance between the adjacent line segments is less than a preset distance threshold, connecting the adjacent line segments into one line segment; Wherein, after S5, the method further includes: marking the connected domains, and different connected domains have different marking symbols.

2. According to the method for segmenting the working scene map area of ​​the cleaning robot described in claim 1, It is characterized in that Obtain a global two-dimensional map of the cleaning robot's working scene, including: The cleaning robot scans the two-dimensional distance information of a certain height of the working scene through a laser sensor, continuously obtains laser data of each time frame, and uses the global two-dimensional grid map of the cleaning robot's working scene constructed by the SLAM method as the global two-dimensional map.

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