Cleaning robot and mapping method thereof

Abstract

The present invention discloses a mapping method for a cleaning robot, the cleaning robot includes a laser ranging sensor, and the mapping method includes the following steps: obtaining laser point cloud data for building a map through the laser ranging sensor; for each laser point data in the laser point cloud data, using a preset window to select laser point data containing the laser point data and adjacent to the laser point data; determining the correlation coefficient between the laser point data and the laser point data adjacent to the laser point data; classifying the laser point data according to the size of the correlation coefficient; filtering out the laser point data in the category where the number of laser points is less than a preset threshold from the laser point cloud data; and building a map based on the remaining laser point data. The aforementioned mapping method disclosed in the present invention can effectively avoid erroneously removing noise points and ensure the integrity of the mapping result.

Description

Technical Field

[0001] The present disclosure relates to the technical field of smart home products, and in particular to a cleaning robot and a mapping method thereof. Background Art

[0002] Most cleaning robots can now build maps of the environment, and then can autonomously navigate according to the built maps to clean the room environment. Among them, cleaning robots mainly include sweeping robots, mopping robots, sweeping, vacuuming and mopping all-in-one machines, etc.

[0003] Existing cleaning robots generally use laser ranging sensors to scan the environment, and then build maps based on the scanned laser point cloud data. Due to obstacles in the environment, uneven walls, and measurement errors, there will be noise point data in the laser point cloud data, which will affect the map building results of the cleaning robot.

[0004] In order to filter out noise points, the K-MEANS algorithm (an iterative clustering analysis algorithm) is usually used in the prior art: first, multiple laser point data with similar distances are classified into one category, and then the laser point data in the category with less laser point data is filtered out, thereby achieving the purpose of eliminating noise points.

[0005] However, when using the K-MEANS algorithm, smaller objects are often mistakenly filtered out, resulting in inaccurate map construction (such as missing objects). For example, if there are table legs in the environment (such as Figure 1 As shown in the figure). Since there are fewer laser points hitting the table legs, and the laser points on the table legs are far away from other laser points in the environment, the laser points hitting the table legs can only be classified into a separate category. Since there are fewer laser points of this type, they are often filtered out, resulting in the absence of the table legs in the constructed map (such as Figure 2 shown). Summary of the invention

[0006] The present disclosure provides a cleaning robot and a mapping method thereof, which are used to solve the following technical problems in the prior art: when noise is filtered out of laser point cloud data scanned by the existing cleaning robots, the filtering is often improper, which leads to inaccurate maps being constructed.

[0007] In a first aspect, the present disclosure provides a mapping method for a cleaning robot, wherein the cleaning robot includes a laser ranging sensor, and the mapping method includes the following steps:

[0008] Obtain laser point cloud data for map construction through laser ranging sensors;

[0009] For each laser point data in the aforementioned laser point cloud data, a preset window is used to select laser point data including the laser point data and laser point data adjacent to the laser point data;

[0010] Determine a correlation coefficient between the laser point data and the laser point data adjacent to the laser point data, wherein the correlation coefficient is used to characterize the distance between the position represented by the laser point data and the position represented by the laser point data adjacent to the laser point data;

[0011] Classify the aforementioned laser point data according to the size of the correlation coefficient;

[0012] From the aforementioned laser point cloud data, filter out the laser point data in the category where the number of laser points is less than a preset threshold;

[0013] Build a map from the remaining laser point data.

[0014] Optionally, the number of laser point data selected by the aforementioned preset window is less than or equal to the number of laser point data detected by the aforementioned laser ranging sensor for an object of a preset size.

[0015] Optionally, the aforementioned laser point data adjacent to the laser point data is laser point data adjacent to both sides of the laser point data.

[0016] Optionally, the aforementioned determining the correlation coefficient between the laser point data and the laser point data adjacent to the laser point data includes:

[0017] Calculate the correlation coefficient between the laser point data and the laser point data adjacent to the laser point data according to a preset formula and the laser point data and the laser point data adjacent to the laser point data;

[0018] The aforementioned correlation coefficient is positively correlated / negatively correlated with each distance, and each distance is the distance between the position represented by the laser point data and the position represented by each adjacent laser point data.

[0019] Optionally, calculating the correlation coefficient between the laser point data and the laser point data adjacent to the laser point data according to a preset formula, as well as the laser point data and the laser point data adjacent to the laser point data, includes:

[0020] The square of the sum of the differences between the laser point data and each adjacent laser point data is calculated as the correlation coefficient.

[0021] Optionally, the aforementioned classification of the laser point data according to the magnitude of the correlation coefficient includes:

[0022] The laser point data whose correlation coefficient difference is within a preset range are divided into one category.

[0023] Optionally, the aforementioned acquisition of laser point cloud data for constructing a map by a laser ranging sensor includes:

[0024] The aforementioned laser ranging sensor is used to scan the environment to obtain laser point cloud data.

[0025] Optionally, the aforementioned laser ranging sensor is a triangulation laser ranging sensor.

[0026] Optionally, the aforementioned laser point data adjacent to the laser point data are distributed on both sides of the aforementioned laser point data in equal number.

[0027] In a second aspect, the present disclosure provides a cleaning robot, the cleaning robot comprising a processor, a memory, and an execution instruction stored in the memory, wherein the execution instruction is configured to enable the cleaning robot to perform any one of the mapping methods in the first aspect when executed by the processor.

[0028] Based on the foregoing description, it can be understood by those skilled in the art that the present disclosure selects a certain laser point data and laser point data adjacent to the laser point data through a preset window, then determines the correlation coefficient between the laser point data and the laser point data adjacent to the laser point data, and then classifies all laser point data according to the size of the correlation coefficient, and then filters out the laser point data in the category where the number of laser points is less than the preset threshold from the laser point cloud data, thereby filtering out the noise points in the laser point cloud data, and then constructs a map based on the remaining laser point data. Since no matter what size the object is, no matter how the positional relationship between the objects is, as long as the laser ranging sensor can detect the laser point data thereon, the correlation coefficient of the laser point data can be calculated, and the laser point data that can characterize the real position of the object have a small difference in correlation coefficient regardless of the distance, and can be classified into one category and will not be mistakenly filtered out. Therefore, the mapping method disclosed in the present disclosure can not only remove the noise in the laser point cloud data, but also avoid the situation of eliminating the correct laser point data, thereby ensuring the integrity of the mapping.

[0029] Furthermore, by setting the number of laser point data selected by the preset window to be less than or equal to the number of laser point data detected by the laser ranging sensor for an object of a preset size, at least one of the laser point data detected by the laser ranging sensor from the object of the preset size can be calculated with a correlation coefficient, and then it can be determined based on the correlation coefficient whether the laser point data is needed, and when the laser point data is not filtered out, the object represented by the laser point data is displayed on the map, thereby improving the integrity of the map. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solution of the present disclosure, some embodiments of the present disclosure will be described in detail below in conjunction with the accompanying drawings, in which:

[0031] Figure 1 It is a simple schematic diagram of a cleaning robot in the prior art projecting a laser beam onto a table leg;

[0032] Figure 2 It is a cleaning robot in the prior art Figure 1 The mapping results of the medium environment;

[0033] Figure 3 is a schematic diagram of a scene in which the cleaning robot disclosed herein scans an environment;

[0034] Figure 4 is a schematic diagram of the steps of the mapping method in the first embodiment of the present disclosure;

[0035] Figure 5 This is a schematic diagram of the effect when the cleaning robot scans the table legs through the laser ranging sensor;

[0036] Figure 6 Yes Figure 1 Schematic diagram of the effect of building a map of the environment shown;

[0037] Figure 7 It is a schematic diagram of the structure of the recording supervision device in the second embodiment of the present disclosure.

[0038] List of reference numerals:

[0039] 1. Cleaning robot; 11. Laser ranging sensor; 2. Wall; 3. Table leg; 4. T-shaped wall; 5. Corner; 6. Obstacle; 7. Isolated point. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present disclosure clearer, the technical solutions of the present disclosure will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. It should be understood by those skilled in the art that the embodiments described in the specific embodiments of this section are only part of the embodiments of the present disclosure, rather than all the embodiments of the present disclosure. Based on the embodiments described in the specific embodiments of this section, all other embodiments obtained by ordinary technicians in this field without creative work will not deviate from the technical principles of the present disclosure, and therefore should fall within the scope of protection of the present disclosure.

[0041] It should be noted that in the description of the present disclosure, each functional module can be a physical module composed of multiple structures, components or electronic components, or a virtual module composed of multiple programs; each functional module can be a module that exists independently of each other, or a module divided according to the function of an integral module. It should be understood by those skilled in the art that, under the premise of being able to implement the technical solution described in the present disclosure, the composition, implementation and positional relationship of each functional module will not deviate from the technical principle of the present disclosure no matter how it changes, and therefore should fall within the protection scope of the present disclosure.

[0042] like Figure 3 As shown, the cleaning robot 1 of the present disclosure includes a laser ranging sensor 11, which can scan the environment to shoot a laser beam onto a wall 2 and an obstacle 6 in the environment, and then the laser ranging sensor 11 receives the laser reflected from the wall 2 or the obstacle 6, thereby obtaining laser point data. The collection of laser point data detected by the cleaning robot 1 through the laser ranging sensor 11 is laser point cloud data.

[0043] like Figure 3 As shown, due to the influence of mirror reflection, T-shaped wall 4, corner 5 and strong light, the laser point detected by the laser ranging sensor 11 may contain some erroneous laser point data, that is, Figure 3 The isolated points 7 shown in FIG. The data of these isolated points 7 are random, within the effective detection range of the laser ranging sensor 11, and pass through the T-shaped wall 4 and the corner 5, and cannot truly represent the characteristics of the T-shaped wall 4 and the corner 5. Therefore, these isolated points 7 are noises in the laser point cloud data and need to be filtered out before the cleaning robot 1 builds a map based on the laser point cloud data.

[0044] The laser distance measuring sensor 11 is preferably a triangulation laser distance measuring sensor.

[0045] Some embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0046] In the first embodiment of the present disclosure:

[0047] like Figure 4 As shown, the mapping method of the cleaning robot includes:

[0048] Step S110 , obtaining laser point cloud data for constructing a map through the laser ranging sensor 11 .

[0049] Optionally, the cleaning robot 1 is made to walk in the environment where the map is to be constructed, and the laser ranging sensor 11 is made to scan. After the laser ranging sensor 11 scans the entire environment, the laser point cloud data can be obtained.

[0050] Furthermore, each laser point data in the laser point cloud data has coordinates. For example, the coordinates corresponding to the i-th laser point data are P i (x i ,y i ). It should be noted that the i-th laser point data represents the laser point data with serial number i in the laser point cloud data. Wherein, i is a natural number. The sequence of each laser point in the laser point cloud data can be determined according to the order in which the cleaning robot 1 acquires the laser point data, that is, the i-th laser point data detected has a serial number i. Alternatively, those skilled in the art may also adopt any other feasible rules to define the sequence of laser point data as needed. For example, the sequence of laser point data is defined according to the size of the coordinates of the laser point data. Exemplarily, the laser point data are sequenced row by row along the positive direction of the X-axis and the positive direction of the Y-axis of the plane coordinate system.

[0051] Step S120 : for each laser point data in the laser point cloud data, a preset window is used to select the laser point data including the laser point data and the laser point data adjacent to the laser point data.

[0052] The preset window is set to cover at least 2 laser point data.

[0053] Preferably, the laser point data adjacent to the laser point data are the laser point data adjacent to both sides of the laser point data. Further preferably, the laser point data adjacent to the laser point data are distributed on both sides of the laser point data in equal numbers. In short, the laser point data P i Located in the middle of the preset window. The preset window is as follows:

[0054] …… <![CDATA[P i-2 ]]> <![CDATA[P i-1 ]]> <![CDATA[P i ]]> <![CDATA[P i+1 ]]> <![CDATA[P i+2 ]]> ……

[0055] Table 1

[0056] Furthermore, the preset window is configured so that the number of laser point data selected by it is less than or equal to the number of laser point data detected by the laser ranging sensor for an object of a preset size, so that the preset window can select at least one set of laser point data from the laser point data corresponding to the object of the preset size. The object of the preset size is the smallest object in the environment to be mapped, such as a table leg, a chair leg, a bed leg, etc. It can be seen that after the preset size is determined, the length of the preset window can be determined.

[0057] For example, Figure 5As shown, the smallest object in the environment is set to be the table leg 3, and its diameter (preset size) is 4 cm. The effective measurement of the laser ranging sensor 11 on the cleaning robot 1 is set to 1 meter, and the angular resolution θ of the laser ranging sensor 11 is 0.00785 radians. Then the length of the preset window L≈0.04 / θ=5 (when θ is very small, θ≈tanθ). That is, the laser ranging sensor 11 detects 5 laser point data from the table leg 3, and the preset window can only cover 5 laser point data at most, otherwise it will not be able to cover the 5 laser point data corresponding to the table leg 3 at the same time. Therefore, the preset window is as follows:

[0058] <![CDATA[P i-2 ]]> <![CDATA[P i-1 ]]> <![CDATA[P i ]]> <![CDATA[P i+1 ]]> <![CDATA[P i+2 ]]>

[0059] Table 2

[0060] Taking step S120 as an example, the preset window length is set to 5, then for each laser point data P in the laser point cloud data i , select the laser point data P containing the laser point data and the laser point data adjacent to the laser point data through the preset window i-2 , P i-1 , P i , P i+1 and P i+2 .

[0061] When i=3, the preset window is:

[0062] <![CDATA[P1]]> <![CDATA[P2]]> <![CDATA[P3]]> <![CDATA[P4]]> <![CDATA[P5]]>

[0063] Table 3

[0064] When i=4, the preset window is:

[0065] <![CDATA[P2]]> <![CDATA[P3]]> <![CDATA[P4]]> <![CDATA[P5]]> <![CDATA[P6]]>

[0066] Table 4

[0067] When i=5, the preset window is:

[0068] <![CDATA[P3]]> <![CDATA[P4]]> <![CDATA[P5]]> <![CDATA[P6]]> <![CDATA[P7]]>

[0069] Table 5

[0070] It can be seen from Tables 3 to 5 that the preset window collects laser point data in a sliding manner, that is, the preset window slides along the direction in which the laser point data sequence increases, and then collects laser point data. In other words, each time, the laser point data at the head of the preset window (such as the data on the left end of Tables 3 to 5) is deleted first, and then the laser point data in the preset window is slid to the left as a whole, and new laser point data is added to the tail of the preset window (such as the data on the right end of Tables 3 to 5).

[0071] Step S130, determining a correlation coefficient between the laser point data and laser point data adjacent to the laser point data.

[0072] The correlation coefficient is used to characterize the distance between the position represented by the laser point data and the position represented by the adjacent laser point data, and the correlation coefficient is positively correlated / negatively correlated with each distance, and each distance is the distance between the position represented by the laser point data and the position represented by each adjacent laser point data. That is, the larger the correlation coefficient, the larger the distance between the laser point data and the adjacent laser point data; otherwise, the smaller. Alternatively, the larger the correlation coefficient, the smaller the distance between the laser point data and the adjacent laser point data; otherwise, the larger.

[0073] Specifically, according to a preset formula, as well as the laser point data and the laser point data adjacent to the laser point data, a correlation coefficient between the laser point data and the laser point data adjacent to the laser point data is calculated.

[0074] The preset formula is:

[0075]

[0076] In this preset formula, p i is the laser point data in the middle of the preset window, p j is the laser point data except i in the preset window. L is the length of the preset window. It is not difficult to see from the formula that if the laser point data P i With other laser point data P j The more similar the attributes are, the higher the correlation coefficient C between them. i The smaller the difference, the greater the difference.

[0077] In addition, in another feasible solution of this embodiment, the preset formula may also be:

[0078]

[0079] Step S140, classifying the laser point data according to the magnitude of the correlation coefficient.

[0080] Optionally, the laser point data whose correlation coefficient difference is within a preset range are classified into one category. The preset range can be any feasible numerical range, such as [0,5], [0,5.6], [0,13], [0,17.6], etc. For example, the correlation coefficient threshold is set to C threshold , C thresholdis the upper limit of the preset range. If the current laser point data is the first laser point data, create a new category labeled label0, and the number of samples is 1. Calculate the difference between the correlation coefficient C1 of the first laser point data and the correlation coefficient C2 of the second laser point data. If ||C1-C2||<C threshold , the second laser point data is classified into category label0, otherwise a new category label1 is created and the second laser point data is classified into category label1. Calculate the difference between the correlation coefficient C2 of the second laser point data and the correlation coefficient C3 of the third laser point data. If ||C2-C3||<C threshold , the third laser point data is classified into the previous category label0 (the first and second laser point data belong to the same category) or label1 (the first and second laser point data do not belong to the same category), otherwise a new category label1 (the first and second laser point data belong to the same category) or label2 (the first and second laser point data do not belong to the same category) is created. And so on, until all laser point data are classified into corresponding categories.

[0081] Then, similar categories are merged. Specifically, the average value of the correlation coefficients of all laser point data in each category is calculated first, and then all the average values ​​are compared. Finally, the categories corresponding to the average values ​​whose differences are less than the set threshold are classified into one category. For example, the categories before merging are label0, label1, label2, ... label n-2 、label n-1 、label n , there are (n+1) categories in total. Calculate the average value of each category separately and record it as x0, x1, x2, ... x n-2 、x n-1 、x n , and then randomly select an average value and compare it with other average values. If the difference between them is less than the set threshold, the corresponding category of the average value is divided into one category, until all the average values ​​are calculated. Then select an average value from the remaining average values ​​and repeat the above steps.

[0082] The set threshold may be any feasible value, such as 0.3, 5, 9, 25, 30, etc.

[0083] Step S150, filtering out the laser point data in the category where the number of laser points is less than a preset threshold from the laser point cloud data.

[0084] Specifically, the number of laser point data in each category is counted. If the number of laser point data in a category is less than a preset threshold, all laser point data in the category are deleted from the laser point cloud data. The laser point data in the category are isolated points 7 (such as Figure 3 shown).

[0085] The preset threshold is a value obtained based on experimental data. Under the condition of meeting the actual situation, the preset threshold can be any feasible value, such as 3, 8, 13, 35, etc.

[0086] Step S160, constructing a map based on the remaining laser point data.

[0087] Exemplarily, the remaining laser point data are displayed on the map according to their respective corresponding coordinates to represent the walls and obstacles in the environment, thereby completing the construction of the map.

[0088] Using the aforementioned mapping method of this embodiment, Figure 1 The result of mapping the scene shown in Figure 6 As shown. The table leg 3 is displayed as an arc on the map. In fact, due to the scaling of the map and the filtering of the laser point data, the table leg 3 may only retain a few points, so the table leg 3 may not necessarily be displayed as such an arc on the map, and may also be displayed as a point or a line. For example, the number of laser point data detected by the laser ranging sensor 11 from the table leg 3 is 5, and the preset window length is also 5. Then, the above method of this embodiment will filter out the 4 laser point data on both sides of the table leg 3. That is, Figure 1 After noise removal, only one laser point data is retained for table leg 3, resulting in table leg 3 being displayed as a point on the map.

[0089] Based on the foregoing description, those skilled in the art can understand that, in this embodiment, a certain laser point data and laser point data adjacent to the laser point data are selected through a preset window, and then the correlation coefficient between the laser point data and the laser point data adjacent to the laser point data is determined, and then all the laser point data are classified according to the size of the correlation coefficient, and then the laser point data in the category with the number of laser points less than the preset threshold is filtered out from the laser point cloud data, thereby filtering out the noise points in the laser point cloud data, and then constructing a map based on the remaining laser point data. Regardless of the size of the object and the positional relationship between the objects, as long as the laser ranging sensor can detect the laser point data thereon, the correlation coefficient of the laser point data can be calculated, and the laser point data that can represent the real position of the object have a small difference in correlation coefficient regardless of the distance, and can be classified into one category and will not be mistakenly filtered out. Furthermore, by setting the number of laser point data selected by the preset window to be less than or equal to the number of laser point data detected by the laser ranging sensor for an object of a preset size, at least one of the laser point data detected by the laser ranging sensor from the object of the preset size can be calculated with a correlation coefficient, and then it can be determined based on the correlation coefficient whether the laser point data is needed, and when the laser point data is not filtered out, the object represented by the laser point data is displayed on the map, thereby improving the integrity of the map.

[0090] In the second embodiment of the present disclosure:

[0091] like Figure 7 As shown, the present disclosure also provides a cleaning robot. The cleaning robot includes a processor at the hardware level, and optionally also includes a memory and a bus. In addition, the cleaning robot is also allowed to include hardware required by other services.

[0092] The memory is used to store execution instructions, which are specifically executable computer programs. Further, the memory may include a memory and a non-volatile memory, and provide execution instructions and data to the processor. Exemplarily, the memory may be a high-speed random access memory (RAM), and the non-volatile memory may be at least one disk storage.

[0093] The bus is used to connect the processor, memory and network interface to each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Although only one bidirectional arrow is used in the diagram, this does not mean that there is only one bus or only one type of bus.

[0094] In a feasible implementation of the above-mentioned cleaning robot, the processor may first read the corresponding execution instruction from the non-volatile memory into the memory and then run it, or may first obtain the corresponding execution instruction from other devices and then run it. When the processor executes the execution instruction stored in the memory, it can implement the mapping method in any of the above-mentioned mapping method embodiments of the present disclosure.

[0095] It will be understood by those skilled in the art that the above-mentioned mapping method can be applied to a processor or implemented with the aid of a processor. Exemplarily, a processor is an integrated circuit chip having the ability to process signals. In the process of the processor executing the above-mentioned mapping method, each step of the above-mentioned mapping method can be completed by an integrated logic circuit in the form of hardware or an instruction in the form of software in the processor. Further, the above-mentioned processor can be a general-purpose processor, such as a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, microprocessors, and any other conventional processors.

[0096] Those skilled in the art can also understand that the steps of the above-mentioned mapping method embodiment of the present disclosure can be executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. Among them, the software module can be located in a random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, register, and other mature storage media in the field. The storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the execution of the steps in the above-mentioned mapping method embodiment.

[0097] Those skilled in the art can understand that the electronic device described in the above embodiments of the present disclosure may be a computer.

[0098] So far, the description of the technical solution of the present disclosure has been completed with reference to the accompanying drawings and in combination with the above embodiments.

[0099] It is understood by those skilled in the art that the above-mentioned record supervision method embodiment of the present disclosure can be presented in the form of a record supervision method or a computer program product. Therefore, the technical solution of the present disclosure can be implemented in the form of all hardware, or in the form of all software, or in the form of a combination of software and hardware.

Claims

1. A method for mapping a cleaning robot, the cleaning robot comprising a laser ranging sensor, characterized in that: The mapping method comprises the following steps: Obtain laser point cloud data for map construction through laser ranging sensors; For each laser point data in the laser point cloud data, a preset window is used to select laser point data including the laser point data and laser point data adjacent to the laser point data; the number of laser point data selected by the preset window is less than or equal to the number of laser point data detected by the laser ranging sensor for an object of a preset size; Determine a correlation coefficient between the laser point data and laser point data adjacent to the laser point data, wherein the correlation coefficient is used to characterize the sum of the distances between the laser point data and each laser point data adjacent to the laser point data; wherein the correlation coefficient is positively correlated / negatively correlated with the distance; Classifying the laser point data according to the magnitude of the correlation coefficient includes: classifying the laser point data whose correlation coefficient difference is within a preset range into one category; otherwise, creating a new category until all the laser point data are classified into the corresponding category; Filtering out the laser point data in the category where the number of laser points is less than a preset threshold from the laser point cloud data; Build a map from the remaining laser point data.

2. The mapping method according to claim 1, characterized in that: The laser point data adjacent to the laser point data are laser point data adjacent to both sides of the laser point data.

3. The mapping method according to claim 1, characterized in that: The step of determining the correlation coefficient between the laser point data and the laser point data adjacent to the laser point data comprises: Calculate the correlation coefficient between the laser point data and the laser point data adjacent to the laser point data according to a preset formula and the laser point data and the laser point data adjacent to the laser point data; The correlation coefficient is positively correlated / negatively correlated with each distance, and each distance is the distance between the position represented by the laser point data and the position represented by each adjacent laser point data.

4. The mapping method according to claim 3, characterized in that: Calculating the correlation coefficient between the laser point data and the laser point data adjacent to the laser point data according to a preset formula, including: The square of the sum of the differences between the laser point data and each adjacent laser point data is calculated as the correlation coefficient.

5. The mapping method according to any one of claims 1 to 4, characterized in that: The laser point cloud data for constructing a map is obtained by using a laser ranging sensor, including: The laser ranging sensor is used to scan the environment to obtain laser point cloud data.

6. The mapping method according to any one of claims 1 to 4, characterized in that: The laser distance measuring sensor is a triangulation laser distance measuring sensor.

7. The mapping method according to claim 2, characterized in that: The laser point data adjacent to the laser point data are distributed on both sides of the laser point data in equal numbers.

8. A cleaning robot, characterized in that: The cleaning robot comprises a processor, a memory and execution instructions stored in the memory, wherein the execution instructions are configured to enable the cleaning robot to perform the mapping method according to any one of claims 1 to 7 when executed by the processor.

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