Obstacle recognition method, device, equipment, medium and weeding robot

Abstract

The embodiment of the present invention discloses an obstacle recognition method, device, equipment, medium and weeding robot. The method comprises: obtaining chromaticity information of a candidate weeding area image; generating a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtaining peak information and chromaticity range information of the target chromaticity histogram; determining whether there is an obstacle in the candidate weeding area image according to the peak information and chromaticity range information. By running the technical solution provided by the embodiment of the present invention, the problem that the boundary of the weeding area of ​​the weeding robot is usually calibrated by burying boundary lines in the prior art can be solved, which consumes a lot of manpower and material resources and increases costs. In addition, since there are restrictions on the burying of boundary lines, the shape of the weeding area is limited to a certain extent, thereby achieving the effect of improving the recognition efficiency and accuracy of obstacles in the candidate weeding area of ​​the weeding robot.

Description

Technical Field

[0001] The embodiments of the present invention relate to computer technology, and more particularly to an obstacle recognition method, device, equipment, medium and weeding robot. Background Art

[0002] With the improvement of living standards, people are paying more and more attention to environmental construction, so the construction of urban greening gardens has received more and more attention. At the same time, efficient greening maintenance, such as daily weeding, has gradually become a demand. However, since traditional weeders require manual control, weeding robots with autonomous working functions are gradually emerging.

[0003] In the prior art, the boundary of the weeding area of ​​the weeding robot is usually calibrated by burying boundary lines, which consumes a lot of manpower and material resources and increases costs. In addition, since there are restrictions on burying boundary lines, such as the angle of the corner cannot be less than 90 degrees, the shape of the weeding area is limited to a certain extent. Summary of the invention

[0004] The embodiments of the present invention provide an obstacle recognition method, device, equipment, medium and a weeding robot, so as to achieve the effect of improving the recognition efficiency and accuracy of obstacles in a candidate weeding area of ​​the weeding robot.

[0005] In a first aspect, an embodiment of the present invention provides an obstacle recognition method, the method comprising:

[0006] Acquire the chromaticity information of the candidate weeding area image;

[0007] Generating a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and acquiring peak information and chromaticity range information of the target chromaticity histogram;

[0008] It is determined whether there is an obstacle in the candidate weeding area image according to the peak information and the chromaticity range information.

[0009] In a second aspect, an embodiment of the present invention further provides an obstacle recognition device, the device comprising:

[0010] An information acquisition module, used to acquire chromaticity information of the candidate weeding area image;

[0011] A histogram generation module, used to generate a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtain peak information and chromaticity range information of the target chromaticity histogram;

[0012] The obstacle determination module is used to determine whether there is an obstacle in the candidate weeding area image according to the peak information and the chromaticity range information.

[0013] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0014] one or more processors;

[0015] a storage device for storing one or more programs,

[0016] When the one or more programs are executed by the one or more processors, the one or more processors implement the obstacle recognition method as described above.

[0017] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the obstacle recognition method as described above when executed by a processor.

[0018] In a fifth aspect, an embodiment of the present invention further provides a weeding robot, comprising a robot body and the above-mentioned electronic device.

[0019] The embodiment of the present invention obtains the chromaticity information of the candidate weeding area image; generates the target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtains the peak information and chromaticity range information of the target chromaticity histogram; determines whether there are obstacles in the candidate weeding area image according to the peak information and chromaticity range information, thereby solving the problem that the boundary of the weeding area of ​​the weeding robot is usually calibrated by burying boundary lines in the prior art, which consumes a lot of manpower and material resources and increases costs. In addition, since there are restrictions on the burying of boundary lines, the shape of the weeding area is limited to a certain extent, thereby achieving the effect of improving the recognition efficiency and accuracy of obstacles in the candidate weeding area of ​​the weeding robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flowchart of an obstacle identification method provided in Embodiment 1 of the present invention;

[0021] Figure 2 A flowchart of an obstacle identification method provided in Embodiment 2 of the present invention;

[0022] Figure 3 A schematic diagram of the structure of an obstacle recognition device provided in Embodiment 3 of the present invention;

[0023] Figure 4 A schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0025] Embodiment 1

[0026] Figure 1 This is a flowchart of an obstacle identification method provided in the first embodiment of the present invention. This embodiment is applicable to the situation where a weeding robot identifies obstacles in a candidate weeding area. The method can be executed by the obstacle identification device provided in the embodiment of the present invention, and the device can be implemented in software and / or hardware. Figure 1 The obstacle recognition method provided in this embodiment includes:

[0027] Step 110: Obtain chromaticity information of the candidate weeding area image.

[0028] The candidate weeding area is the possible working area of ​​the weeding robot, which may be entirely filled with weeds to be removed, i.e., the weeding area; or it may be an obstacle area with a color similar to that of the grass, such as yellow or green cardboard or large leaves.

[0029] The candidate weeding area image can be taken by a camera installed on the weeding robot, and this embodiment does not limit this. The chromaticity information of the candidate weeding area image is the chromaticity characteristics of the entire candidate weeding area image, and the chromaticity-related information in the image can be obtained by obtaining the chromaticity channel image of the candidate weeding area image, such as the chromaticity value of each pixel in the image, and this embodiment does not limit this.

[0030] Step 120: Generate a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtain peak information and chromaticity range information of the target chromaticity histogram.

[0031] A target chromaticity histogram of the candidate weeding area image is generated according to the chromaticity information, wherein the target chromaticity histogram is a histogram generated according to the chromaticity information and used to intuitively reflect the chromaticity distribution information in the candidate weeding area image. The horizontal axis of the histogram can be the chromaticity value, and the vertical axis can be the frequency, which is used to reflect the chromaticity distribution of pixel points in the candidate weeding area image.

[0032] The peak information of the target chromaticity histogram is information related to the peak in the target chromaticity histogram, which may be a specific value of the peak in the target chromaticity histogram, and this embodiment does not limit this. The chromaticity range information of the target chromaticity histogram is chromaticity interval information related to obstacle recognition in the target chromaticity histogram.

[0033] In this embodiment, optionally, generating a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtaining peak information of the target chromaticity histogram includes:

[0034] Performing histogram statistics on the chromaticity information to generate a to-be-processed chromaticity histogram of the candidate weeding area image;

[0035] Smoothing the chromaticity histogram to be processed to obtain a smoothed chromaticity histogram;

[0036] The target chromaticity histogram is generated according to the smoothed chromaticity histogram, and peak information of the target chromaticity histogram is obtained.

[0037] By performing histogram statistics on the chromaticity information, a chromaticity histogram to be processed of the candidate weeding area image is directly generated, wherein the chromaticity histogram to be processed can reflect the specified chromaticity information of the pixel points in the candidate weeding area image, and exemplarily, it is the chromaticity value distribution statistics of all the pixel points in the candidate weeding area image, which is not limited in this embodiment.

[0038] The chromaticity histogram to be processed is smoothed to remove noise in the chromaticity histogram to be processed, thereby improving the accuracy of obtaining peak information in the target chromaticity histogram subsequently generated according to the smoothed chromaticity histogram. The smoothing process may be a filtering process, which is not limited in this embodiment.

[0039] The data in the smoothed chromaticity histogram can be filtered, a target chromaticity histogram can be generated according to the filtered data, and peak information of the target chromaticity histogram can be obtained to improve the pertinence and accuracy of peak information acquisition.

[0040] In this embodiment, optionally, generating the target chromaticity histogram according to the smoothed chromaticity histogram, and acquiring peak information of the target chromaticity histogram includes:

[0041] Determine a target peak point set and a target valley point set in a preset chromaticity interval of the smoothed chromaticity histogram according to a preset screening rule;

[0042] The target chromaticity histogram is generated according to the target peak point set and the target valley point set, and peak information of the target chromaticity histogram is obtained.

[0043] Among them, the target peak point set and the target valley point set are obtained by screening all peak points and valley points in a preset chromaticity interval in the smooth chromaticity histogram through a preset screening rule to generate a target chromaticity histogram. Optionally, the preset chromaticity interval is 15-95, which is not limited in this embodiment. The target chromaticity histogram is generated according to the target peak point set and the target valley point set, and the generated target chromaticity histogram is more targeted for obstacle identification, so as to improve the targeting and accuracy of peak information acquisition.

[0044] In this embodiment, optionally, the preset screening rules include:

[0045] Each peak value in the target peak point set is greater than a preset multiple of each valley value in the target valley value point set;

[0046] The distance between the target peak points is greater than a preset distance threshold;

[0047] The peak value of the target peak point is greater than a preset peak threshold.

[0048] Exemplarily, the peak value of the target peak point is greater than K*the valley value of the target valley point, where K is a preset multiple. The distance between the target peak points is greater than a preset distance threshold D, and the peak value of the target peak point is greater than a preset peak threshold M. The peak points and valley points that may be closely related to obstacle identification are screened out through preset screening rules to generate a target chromaticity histogram.

[0049] Step 130: Determine whether there is an obstacle in the candidate weeding area image according to the peak information and the chromaticity range information.

[0050] The chromaticity range information and peak information of the target chromaticity histogram are compared with the preset information judgment conditions. The preset information judgment conditions are related to the chromaticity range information and peak information. If the preset information judgment conditions are met, it is determined that there are obstacles in the candidate weeding area image, so that the weeding robot can perform subsequent obstacle processing.

[0051] Exemplarily, the identified obstacle is a yellow or green cardboard or a large leaf that is similar in color to the grass.

[0052] The technical solution provided in this embodiment obtains the chromaticity information of the candidate weeding area image; generates the target chromaticity histogram of the candidate weeding area image based on the chromaticity information, and obtains the peak information and chromaticity range information of the target chromaticity histogram; determines whether there are obstacles in the candidate weeding area image based on the peak information and chromaticity range information, thereby solving the problem that the boundary of the weeding area of ​​the weeding robot is usually calibrated by burying boundary lines in the prior art, which consumes a lot of manpower and material resources and increases costs. In addition, since there are restrictions on the burying of boundary lines, the shape of the weeding area is limited to a certain extent, thereby achieving the effect of improving the recognition efficiency and accuracy of obstacles in the candidate weeding area of ​​the weeding robot.

[0053] Embodiment 2

[0054] Figure 2 This is a flowchart of an obstacle identification method provided in Embodiment 2 of the present invention. This technical solution is to supplement the process of determining whether there is an obstacle in the candidate weeding area image based on the peak information and chromaticity range information. Compared with the above solution, this solution is specifically optimized as follows: the peak information includes: the maximum peak value; the chromaticity range information includes: the target chromaticity range;

[0055] Determining whether there is an obstacle in the candidate weeding area image according to the peak information and the chromaticity range information includes:

[0056] If the ratio of the maximum peak value to the target chromaticity range is greater than a preset ratio threshold, it is determined that there is an obstacle in the candidate weeding area image. Specifically, the flowchart of the obstacle identification method is as follows: Figure 2 As shown:

[0057] Step 210: Obtain chromaticity information of the candidate weeding area image.

[0058] Step 220: Generate a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtain peak information of the target chromaticity histogram; the peak information includes: a maximum peak; the chromaticity range information includes: a target chromaticity range.

[0059] Among them, the maximum peak is the maximum value of all peaks in the target chromaticity histogram, which indicates the concentration of chromaticity values ​​in the candidate weeding area image. The larger the value, the more pixels of the chromaticity value, that is, the more concentrated the chromaticity distribution.

[0060] The target chromaticity range is a chromaticity interval that meets preset conditions in the target chromaticity histogram. For example, it is a chromaticity interval whose frequency is greater than a preset peak threshold M. This embodiment does not impose any limitation on this.

[0061] Step 230: If the ratio of the maximum peak value to the target chromaticity range is greater than a preset ratio threshold, it is determined that there is an obstacle in the candidate weeding area image.

[0062] For example, the maximum peak is recorded as maxH, the target chromaticity range is recorded as width, and the ratio of the maximum peak to the target chromaticity range is maxH / width; if the preset ratio value threshold is 600, the preset information judgment condition is maxH / width>600. When this condition is met, it is determined that there are obstacles such as yellow or green cardboard or large leaves that are similar in color to the grass in the candidate weeding area image. The preset information judgment condition can be adjusted according to the specific judgment scenario, and this embodiment does not limit this.

[0063] The embodiment of the present invention determines whether there are obstacles in the image of the candidate weeding area through the maximum peak value and the target chromaticity range, so as to solve the problem that when obstacles or boundaries with similar colors to the grass sometimes appear on the grass, it is easy to cause misjudgment by judging the obstacles only by a single chromaticity peak value, thereby improving the recognition efficiency and accuracy of obstacles with similar colors to the grass in the candidate weeding area of ​​the weeding robot.

[0064] Embodiment 3

[0065] Figure 3 This is a schematic diagram of the structure of an obstacle identification device provided in the third embodiment of the present invention. The device can be implemented by hardware and / or software, can execute an obstacle identification method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. Figure 3 As shown, the device comprises:

[0066] The information acquisition module 310 is used to acquire the chromaticity information of the candidate weeding area image;

[0067] A histogram generation module 320 is used to generate a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtain peak information and chromaticity range information of the target chromaticity histogram;

[0068] The obstacle determination module 330 is used to determine whether there is an obstacle in the candidate weeding area image according to the peak information and the chromaticity range information.

[0069] The technical solution provided in this embodiment obtains the chromaticity information of the candidate weeding area image; generates the target chromaticity histogram of the candidate weeding area image based on the chromaticity information, and obtains the peak information and chromaticity range information of the target chromaticity histogram; determines whether there are obstacles in the candidate weeding area image based on the peak information and chromaticity range information, thereby solving the problem that the boundary of the weeding area of ​​the weeding robot is usually calibrated by burying boundary lines in the prior art, which consumes a lot of manpower and material resources and increases costs. In addition, since there are restrictions on the burying of boundary lines, the shape of the weeding area is limited to a certain extent, thereby achieving the effect of improving the recognition efficiency and accuracy of obstacles in the candidate weeding area of ​​the weeding robot.

[0070] On the basis of the above technical solutions, optionally, the histogram generation module includes:

[0071] A first histogram generating unit, used for performing histogram statistics on the chromaticity information to generate a to-be-processed chromaticity histogram of the candidate weeding area image;

[0072] A histogram filtering unit, used for smoothing the chromaticity histogram to be processed to obtain a smoothed chromaticity histogram;

[0073] The second histogram generating unit is used to generate the target chromaticity histogram according to the smoothed chromaticity histogram and obtain the peak information of the target chromaticity histogram.

[0074] Based on the above technical solutions, optionally, the second histogram generating unit includes:

[0075] A set determination subunit, used to determine a target peak point set and a target valley point set in a preset chromaticity interval of the smoothed chromaticity histogram according to a preset screening rule;

[0076] The histogram generating subunit is used to generate the target chromaticity histogram according to the target peak point set and the target valley point set, and obtain the peak information of the target chromaticity histogram.

[0077] On the basis of the above technical solutions, optionally, the preset screening rules include:

[0078] Each peak value in the target peak point set is greater than a preset multiple of each valley value in the target valley value point set;

[0079] The distance between the target peak points is greater than a preset distance threshold;

[0080] The peak value of the target peak point is greater than a preset peak threshold.

[0081] Based on the above technical solutions, optionally, the peak information includes: a maximum peak value; the chromaticity range information includes: a target chromaticity range;

[0082] The obstacle determination module comprises:

[0083] The obstacle determination unit is configured to determine that an obstacle exists in the candidate weeding area image if a ratio value of the maximum peak value to the target chromaticity range is greater than a preset ratio value threshold.

[0084] Embodiment 4

[0085] Figure 4 A schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention is shown in FIG. Figure 4 As shown, the electronic device includes a processor 40, a memory 41, an input device 42 and an output device 43; the number of processors 40 in the electronic device can be one or more. Figure 4 A processor 40 is taken as an example; the processor 40, the memory 41, the input device 42 and the output device 43 in the electronic device can be connected by a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0086] The memory 41 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the obstacle recognition method in the embodiment of the present invention. The processor 40 executes various functional applications and data processing of the electronic device by running the software programs, instructions and modules stored in the memory 41, that is, implements the above-mentioned obstacle recognition method.

[0087] The memory 41 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 41 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 41 may further include a memory remotely arranged relative to the processor 40, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0088] Embodiment 5

[0089] Embodiment 5 of the present invention further provides a storage medium including computer executable instructions, wherein the computer executable instructions are used to execute an obstacle identification method when executed by a computer processor, the method comprising:

[0090] Acquire the chromaticity information of the candidate weeding area image;

[0091] Generating a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and acquiring peak information and chromaticity range information of the target chromaticity histogram;

[0092] It is determined whether there is an obstacle in the candidate weeding area image according to the peak information and the chromaticity range information.

[0093] Of course, the computer executable instructions of a storage medium containing computer executable instructions provided in an embodiment of the present invention are not limited to the operations of the method described above, and can also execute related operations in the obstacle identification method provided in any embodiment of the present invention.

[0094] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0095] It is worth noting that in the embodiment of the above-mentioned obstacle recognition device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0096] Embodiment 6

[0097] Embodiment 6 of the present invention provides a weeding robot, comprising a robot body and the electronic device described in any embodiment of the present invention.

[0098] Specifically, the electronic device installed on the lawn mowing robot can perform relevant operations of an obstacle recognition method described in any embodiment of the present invention.

[0099] The robot body may include two active driving wheels on the left and right, which may be driven by motors respectively. The motors may be brushless motors with reduction gears and Hall sensors. The robot body realizes driving operations such as forward, backward, turning and arc by controlling the speed and direction of the two active driving wheels. The robot body also includes universal wheels, cameras and rechargeable batteries, wherein the universal wheels play a supporting and balancing role. The camera is installed at a designated position of the robot and forms a preset angle with the horizontal direction to capture images of the candidate weeding area. The rechargeable battery is used to provide power for the robot to work.

[0100] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. An obstacle recognition method, characterized in that: include: Acquire the chromaticity information of the candidate weeding area image; Generating a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and acquiring peak information and chromaticity range information of the target chromaticity histogram; Determining whether there is an obstacle in the candidate weeding area image according to the peak information and the chromaticity range information; Wherein, the peak information includes: maximum peak value; the chromaticity range information includes: target chromaticity range; The determining whether there is an obstacle in the candidate weeding area image according to the peak information and the chromaticity range information includes: If the ratio of the maximum peak value to the target chromaticity range is greater than a preset ratio threshold, it is determined that an obstacle exists in the candidate weeding area image.

2. The method according to claim 1, characterized in that Generating a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and acquiring peak information and chromaticity range information of the target chromaticity histogram, including: Performing histogram statistics on the chromaticity information to generate a to-be-processed chromaticity histogram of the candidate weeding area image; Smoothing the chromaticity histogram to be processed to obtain a smoothed chromaticity histogram; The target chromaticity histogram is generated according to the smoothed chromaticity histogram, and peak information and chromaticity range information of the target chromaticity histogram are obtained.

3. The method according to claim 2, characterized in that Generating the target chromaticity histogram according to the smoothed chromaticity histogram, and acquiring peak information and chromaticity range information of the target chromaticity histogram, including: Determine a target peak point set and a target valley point set in a preset chromaticity interval of the smoothed chromaticity histogram according to a preset screening rule; The target chromaticity histogram is generated according to the target peak point set and the target valley point set, and peak information and chromaticity range information of the target chromaticity histogram are obtained.

4. The method according to claim 3, characterized in that: The preset screening rules include: Each peak value in the target peak point set is greater than a preset multiple of each valley value in the target valley value point set; The distance between the target peak points is greater than a preset distance threshold; The peak value of the target peak point is greater than a preset peak threshold.

5. An obstacle recognition device, characterized in that: include: An information acquisition module, used to acquire chromaticity information of the candidate weeding area image; A histogram generation module, used to generate a target chromaticity histogram of the candidate weeding area image according to the chromaticity information, and obtain peak information and chromaticity range information of the target chromaticity histogram; An obstacle determination module, used for determining whether there is an obstacle in the candidate weeding area image according to the peak information and the chromaticity range information; Wherein, the peak information includes: maximum peak value; the chromaticity range information includes: target chromaticity range; The obstacle determination module comprises: The obstacle determination unit is configured to determine that an obstacle exists in the candidate weeding area image if a ratio value of the maximum peak value to the target chromaticity range is greater than a preset ratio value threshold.

6. The device according to claim 5, characterized in that The histogram generation module comprises: A first histogram generating unit, used for performing histogram statistics on the chromaticity information to generate a to-be-processed chromaticity histogram of the candidate weeding area image; A histogram filtering unit, used for smoothing the chromaticity histogram to be processed to obtain a smoothed chromaticity histogram; The second histogram generating unit is used to generate the target chromaticity histogram according to the smoothed chromaticity histogram and obtain the peak information of the target chromaticity histogram.

7. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the obstacle recognition method as described in any one of claims 1 to 4.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the obstacle recognition method as described in any one of claims 1 to 4 is implemented.

9. A weeding robot, comprising a robot body, characterized in that: Also included is an electronic device as claimed in claim 7.

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