Three-dimensional point cloud projection and denoising method based on sector area

Abstract

The invention relates to the technical field of navigation map construction, in particular to a three-dimensional point cloud projection and denoising method based on a fan-shaped region, which comprises the following steps: acquiring three-dimensional point cloud information by using various sensors, performing vertical projection on potential three-dimensional obstacle point cloud, and converting the point cloud from a three-dimensional state to a two-dimensional state, obtaining position information of the point cloud in a two-dimensional polar coordinate system; constructing a grid system based on a fan-shaped region, and performing discretization segmentation on a space region at a proper step length on a polar radius and a polar angle; traversing the point cloud information in the two-dimensional state, counting the number of point clouds falling into each grid, calculating the point cloud density of each grid, and if the point cloud density of a certain grid exceeds a threshold value, confirming that the point clouds of the grid belong to obstacles, and outputting the point clouds; and otherwise, determining noisy points, filtering, efficiently fusing ground point cloud information to form a unified ground plane, and ensuring that ground plane estimation is accurate and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of navigation map construction, and in particular to a three-dimensional point cloud projection and denoising method based on a sector area. Background Art

[0002] 3D point cloud projection and denoising is a technology that uses data acquired by multiple sensors (such as lidar, depth cameras, ultrasonic radar, etc.) to combine the 3D point cloud information of multiple sensors to extract information about ground obstacles. This method is commonly used in fields such as 2D map production, robot environmental perception, and robot path planning. The 3D point cloud projection and denoising method based on fan-shaped areas can obtain more comprehensive environmental information, improving the efficiency and accuracy of areas such as robot autonomous obstacle avoidance and path planning. However, this method also faces challenges such as high data fusion complexity, algorithm selection, and parameter tuning. In practical applications, it is necessary to select appropriate methods and tools according to specific scenarios and needs to achieve ground point cloud extraction.

[0003] Existing 3D point cloud projection and denoising methods primarily focus on various point cloud clustering or point cloud object recognition algorithms, making them inadequate for mobile robot applications where computing resources are limited. To enable robot environmental awareness and obstacle avoidance, this paper proposes a sector-based 3D point cloud projection and denoising method that efficiently extracts environmental obstacle information. Summary of the Invention

[0004] The purpose of the present invention is to provide a three-dimensional point cloud projection and denoising method based on a sector-shaped area to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A three-dimensional point cloud projection and denoising method based on a sector area, the method comprising:

[0007] S1. Use multiple sensors to obtain 3D point cloud information, vertically project the potential 3D obstacle point cloud, and convert the point cloud from 3D to 2D to obtain the position information of the point cloud in a 2D polar coordinate system;

[0008] S2. Construct a grid system based on sector regions and discretize the spatial region with appropriate step sizes at the polar radius and polar angle;

[0009] S3. Traverse the point cloud information in the two-dimensional state, count the number of point clouds falling into each grid, and calculate the point cloud density of each grid. If the point cloud density of a certain grid exceeds the threshold, it is determined that the point cloud of the grid is an obstacle and output; otherwise, it is determined to be a noise point and filtered.

[0010] Preferably, the S1 includes:

[0011] Obtain obstacle 3D point cloud information The 3D point cloud of the obstacle comes from multiple sensors, including a lidar and several depth cameras. The lidar coordinate system is used as the primary coordinate system and the depth camera coordinate system is used as the auxiliary coordinate system. The point cloud information is vertically projected and their position information in the polar coordinate system is calculated:

[0012] Set a point cloud p∈{x,y,z}, according to the formula: The position information of the point cloud in the polar coordinate system is obtained.

[0013] Preferably, in S2, a sector-based polar coordinate grid system is constructed, including:

[0014] S201, set the range of the current polar coordinate system: polar radius r∈[r min ,r max ], polar angle θ∈[θ min ,θ max ];

[0015] S202. According to the formula, the polar radius is discretized with a step size Δr, and the polar angle is discretized with a step size Δθ, so that the point (r i ,θ j ):

[0016] r i =r min +i×Δr,i=0,1,2,…,N r ,

[0017] θ j =θ min +j×Δθ,j=0,1,2,…,N θ ,

[0018] Preferably, S3 includes:

[0019] S301. Count the number of points falling into each grid. For a point with polar coordinate value (r, θ), the corresponding grid is

[0020] S302: The statistics include not only the current grid but also the adjacent grids, and the grid status needs to be determined based on the density information of the points falling into the above grids;

[0021] Specifically, with the current grid as the center, N grids are expanded forward and backward on the polar radius. neibo,r , extending forward and backward in polar angle N neibo,θ, define the number of points falling into the expanded grid as N f ;

[0022] Its area t area and density value Th density The calculation formula is:

[0023]

[0024] If the density value is greater than the threshold μ, the corresponding point in the current grid will be regarded as an obstacle point and output; otherwise, it will be confirmed as a noise point and filtered.

[0025] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned three-dimensional point cloud projection and denoising method based on a sector area.

[0026] A computer device includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the program, the steps of the above-mentioned three-dimensional point cloud projection and denoising method based on a fan-shaped area are implemented.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] This method addresses applications where mobile robots have limited computing resources. It enables the robot to perceive obstacles and avoid them. By parallelizing the calculation of ground point cloud information from each sensor, the method ensures real-time performance. It efficiently integrates ground point cloud information to form a unified ground plane, ensuring accurate and reliable ground plane estimation. Furthermore, it maintains ground point cloud information based on thresholds, ensuring efficient and accurate ground point cloud extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0030] Figure 1 Schematic diagram of the principle of a three-dimensional point cloud projection and denoising method based on a sector area of the present invention;

[0031] Figure 2 This is a flow chart of a three-dimensional point cloud projection and denoising method based on a fan-shaped area of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] See also Figure 1-Figure 2 , the present invention provides a technical solution:

[0034] Example 1: A three-dimensional point cloud projection and denoising method based on a sector area, comprising the following steps:

[0035] S1, vertically project the potential 3D obstacle point cloud, convert the point cloud from 3D state to 2D state, and obtain the position information of the point cloud in the 2D polar coordinate system;

[0036] As a further preferred embodiment of the present invention, in step S1, the three-dimensional point cloud of the obstacle is derived from multiple sensors, including a laser radar and several depth cameras, with the laser radar coordinate system as the main coordinate system and the depth camera coordinate system as the auxiliary coordinate system. Perform vertical projection and calculate their position information in the polar coordinate system. Taking a point p∈{x,y,z} as an example, the specific calculation process is as follows:

[0037]

[0038] S2, establish a grid system based on sector areas, and discretize the spatial area with appropriate step sizes on the polar radius and polar angle;

[0039] As a further preferred embodiment of the present invention, in step S2, a sector-based polar coordinate grid system is constructed, and the range of the current polar coordinate system is set, and the polar radius r∈[r min ,r max ], polar angle θ∈[θ min ,θ max ], discretize the polar diameter with a step size Δr, and discretize the polar angle with a step size Δθ, then the points in the grid system can be expressed as (r i ,θ j The specific formula is as follows:

[0040] r i =r min +i×Δr,i=0,1,2,…,N r ,

[0041] θ j =θ min+j×Δθ,j=0,1,2,…,N θ ,

[0042] S3, traverses the current two-dimensional point, counts the number of point clouds falling into each grid, and calculates the point cloud density of each grid. If the density exceeds the threshold, the grid point cloud is considered to be an obstacle and is output; otherwise, it is considered to be a noise point and is filtered.

[0043] As a further preferred embodiment of the present invention, in step S3, the number of points falling into each grid is counted. For a point (r, θ) with a polar coordinate value, the grid corresponding to it is The statistics include not only the current grid but also the adjacent grids. The grid status needs to be judged based on the density information of the points falling into the above grids. Specifically, with the current grid as the center, N points are expanded forward and backward on the polar radius. neibo,r , extending forward and backward in polar angle N neibo,θ , define the number of points falling into the expanded grid as N f The calculation formulas for its area and density values are shown below:

[0044]

[0045] If the density value is greater than the threshold, the corresponding point in the current grid will be considered as an obstacle point.

[0046] Example 2: The computer-readable storage medium of this embodiment stores a computer program, which, when executed by a processor, implements the steps of the three-dimensional point cloud projection and denoising method based on a fan-shaped area in Example 1.

[0047] The computer-readable storage medium of this embodiment may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal; the computer-readable storage medium of this embodiment may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, etc. equipped on the terminal; further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device.

[0048] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or is to be output.

[0049] Example 3: The computer device of this embodiment includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of a three-dimensional point cloud projection and denoising method based on a fan-shaped area in Example 1 are implemented.

[0050] In this embodiment, the processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The memory can include read-only memory and random access memory, and provide instructions and data to the processor. A part of the memory can also include non-volatile random access memory. For example, the memory can also store information about the device type.

[0051] Those skilled in the art will appreciate that the disclosed contents of the embodiments may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0052] The present solution is described with reference to the flowcharts and / or block diagrams of the methods and computer program products according to the embodiments of the present solution. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as the combination of the processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions; these computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or methods Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0053] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or methods Figure 1 The function specified in one or more boxes.

[0054] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or methods Figure 1The steps for the function specified in one or more boxes.

[0055] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0056] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A three-dimensional point cloud projection and denoising method based on a sector area, characterized by: The method comprises: S1. Use multiple sensors to obtain 3D point cloud information, vertically project the potential 3D obstacle point cloud, and convert the point cloud from 3D to 2D to obtain the position information of the point cloud in a 2D polar coordinate system; S2. Construct a grid system based on sector regions and discretize the spatial region with appropriate step sizes at the polar radius and polar angle; S3. Traverse the point cloud information in the two-dimensional state, count the number of point clouds falling into each grid, and calculate the point cloud density of each grid. If the point cloud density of a certain grid exceeds the threshold, it is determined that the point cloud of the grid is an obstacle and output; otherwise, it is determined to be a noise point and filtered.

2. The three-dimensional point cloud projection and denoising method based on a sector area according to claim 1, characterized in that: Said S1 comprises: Obtain obstacle 3D point cloud information The 3D point cloud of the obstacle comes from multiple sensors, including a lidar and several depth cameras. The lidar coordinate system is used as the primary coordinate system and the depth camera coordinate system is used as the auxiliary coordinate system. The point cloud information is vertically projected and their position information in the polar coordinate system is calculated: Set a point cloud p∈{x,y,z}, according to the formula: The position information of the point cloud in the polar coordinate system is obtained.

3. The three-dimensional point cloud projection and denoising method based on a sector area according to claim 1, characterized in that: The polar coordinate grid system based on the sector is constructed in S2, including: S201, set the range of the current polar coordinate system: polar radius r∈[r min ,r max ], polar angle θ∈[θ min ,θ max ]; S202. According to the formula, the polar radius is discretized with a step size Δr, and the polar angle is discretized with a step size Δθ, so that the point (r i ,θ j ):

4. The method for 3D point cloud projection and denoising based on a sector region according to claim 1, wherein: The S3 includes: S301. Count the number of points falling into each grid. For a point with polar coordinate value (r, θ), the corresponding grid is S302: The statistics include not only the current grid but also the adjacent grids, and the grid status needs to be determined based on the density information of the points falling into the above grids; Specifically, with the current grid as the center, N grids are expanded forward and backward on the polar radius. neibo,r , extending forward and backward in polar angle N neibo,θ , define the number of points falling into the expanded grid as N f ; Its area t area and density value Th density The calculation formula is: If the density value is greater than the threshold μ, the corresponding point in the current grid will be regarded as an obstacle point and output; otherwise, it will be confirmed as a noise point and filtered.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the three-dimensional point cloud projection and denoising method based on a fan-shaped area are implemented as described in any one of claims 1 to 4.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps of the three-dimensional point cloud projection and denoising method based on a fan-shaped area are implemented as described in any one of claims 1 to 4.

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