CN114708400A - Three-dimensional laser noise reduction method and device, medium and robot - Google Patents
Three-dimensional laser noise reduction method and device, medium and robot Download PDFInfo
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Abstract
The invention provides a three-dimensional laser noise reduction method, a three-dimensional laser noise reduction device, a medium and a robot, wherein the three-dimensional laser noise reduction method comprises the following steps: s1, acquiring a 3D laser point cloud image; s2, down-sampling the 3D laser point cloud data to obtain down-sampled 3D laser point cloud data; s3, converting the down-sampled 3D laser point cloud data into a kd tree; s4, calculating the distance of each point in the down-sampled 3D laser point cloud under a laser coordinate system; s5, calculating the search radius r according to the distance of each point in the laser coordinate systemi(ii) a S6, calculating the nearest neighbor of each point in the point cloud according to the dynamic radius, searching whether the current sphere meets the threshold value of the minimum point number, if so, keeping the point, otherwise, removing the point. The three-dimensional laser noise reduction method can effectively remove noise in snowy weather.
Description
Technical Field
The invention relates to the technical field of laser, in particular to a three-dimensional laser noise reduction method, a three-dimensional laser noise reduction device, a three-dimensional laser noise reduction medium and a robot.
Background
The autonomous navigation mobile robot needs a three-dimensional laser sensor to measure to obtain original data. However, due to the wave band characteristics of the three-dimensional laser sensor, in extreme weather such as rain and snow, a large amount of irregular noise exists in the original data, so that all functions based on the three-dimensional laser sensor are affected.
Aiming at an outdoor patrol inspection robot, original three-dimensional laser point cloud data are generally needed. However, the sparse point cloud generated by the lidar may be corrupted by internal noise and external environmental noise, resulting in inaccurate measurements. And the noise generated is random. So that some special methods are needed for noise removal.
The existing three-dimensional laser point cloud denoising methods comprise the following three methods:
1. and (3) a voxel filtering algorithm: voxel filtering algorithms, also called voxel downsampling. The point cloud in three-dimensional space is discretized into a plurality of cubes. An algorithm that replaces all points in the cube with one point at the center of the cube. The algorithm can effectively reduce the data volume and the number of the points in the point cloud.
2. Static outlier rejection algorithm: the algorithm calculates k nearest neighbors for each point, and calculates the mean and variance of the k nearest neighbors. Assuming that the result is a gaussian distribution, points whose average distance is outside the average and variance of the global distance are considered outliers and removed from the global point cloud.
3. Radius outlier rejection algorithm: the algorithm assumes that each laser point in the original point cloud contains at least a certain number of nearby points in a specified radius neighborhood. And (4) if the original data meets the assumed conditions, the original data is regarded as a normal point and is reserved, and if the original data does not meet the assumed conditions, the original data is rejected. The algorithm has a good eliminating effect on isolated points in the global point cloud.
However, for the outdoor robot, the three methods have different disadvantages:
in the method 1, aiming at a voxel filtering algorithm, because the algorithm only integrates points in a cube, noise can be reduced only and can not be eliminated;
in the method 2, aiming at the static outlier rejection algorithm, as the noise points are irregular, a noise point is probably connected together, and the algorithm cannot reject the noise points;
in the method 3, the radius needs to be set in the method 3, the setting of the radius is difficult to master, noise points are also included when the radius is too large, and other structured point clouds are removed when the radius is too small.
The background description provided herein is for the purpose of generally presenting the context of the disclosure. Unless otherwise indicated herein, the material described in this section is not prior art to the claims in this application and is not admitted to be prior art by inclusion in this section.
Disclosure of Invention
Aiming at the technical problems in the related art, the invention provides a three-dimensional laser noise reduction method, which comprises the following steps:
s1, acquiring a 3D laser point cloud image;
s2, down-sampling the 3D laser point cloud data to obtain down-sampled 3D laser point cloud data;
s3, converting the down-sampled 3D laser point cloud data into a kd tree;
s4, calculating the distance of each point in the down-sampled 3D laser point cloud under a laser coordinate system;
s5, calculating the search radius r according to the distance of each point in the laser coordinate systemi；
S6, calculating the nearest neighbor of each point in the point cloud according to the dynamic radius, searching whether the current sphere meets the threshold value of the minimum point number, if so, keeping the point, otherwise, removing the point.
Specifically, the method also comprises the following steps:
s7, according to the point cloud piMean value mu of neighboring points ofiObtaining the point cloud standard deviation delta from the mean value mu of the whole point cloud2And according to said mean value muiMean value mu of the entire point cloud, point cloud standard deviation delta2Obtaining a threshold value T, deleting and the point cloud piPoints with an average distance from their close points greater than T.
Specifically, the step S7 specifically includes:
s71, calculating a point piMean value mu of nearest k neighboring point distancesi；
S72, according to the mean value muiCalculating the mean value mu of the whole point cloud;
s73, calculating the standard deviation delta of the point cloud2；
S74, calculating threshold T:
T＝μ+β×δ2
and S75, traversing the point cloud, and deleting all points with the average distance to the k adjacent points thereof being more than T.
In particular, the search radius ri：
ri＝diX α x β, where α is the angular resolution in the horizontal direction of the laser and β is a dynamic parameter greater than 1.
In particular, the downsampling is voxel downsampling.
In a second aspect, another embodiment of the present invention discloses a three-dimensional laser noise reduction device, which includes the following units:
the point cloud obtaining unit is used for obtaining a 3D laser point cloud image;
the down-sampling unit is used for down-sampling the 3D laser point cloud data to acquire the down-sampled 3D laser point cloud data;
a Kd tree generation unit for converting the downsampled 3D laser point cloud data into a Kd tree;
the distance calculation unit is used for calculating the distance of each point in the down-sampled 3D laser point cloud under a laser coordinate system;
a search radius calculation unit for calculating a search radius r according to the distance of each point in the laser coordinate systemi；
And the noise removing unit is used for calculating the nearest neighbor of each point in the point cloud according to the dynamic radius, searching whether the threshold value of the minimum point number is met in the current sphere or not, if the current point is met, keeping the point, and if not, rejecting the point.
Specifically, the method further comprises the following units:
an outlier rejection unit for rejecting the point cloud piMean value mu of neighboring points ofiObtaining the point cloud standard deviation delta from the mean value mu of the whole point cloud2And according to said mean value muiMean value mu of the entire point cloud, point cloud standard deviation delta2Obtaining a threshold value T, deleting and the point cloud piPoints with an average distance from their close points greater than T.
Specifically, the outlier rejection unit further includes:
a point cloud mean calculation unit for calculating a point piMean value mu of nearest k neighboring point distancesi；
A whole point cloud mean calculation unit for calculating the mean mu according to the point cloud meaniCalculating the mean value mu of the whole point cloud;
a point cloud standard deviation calculating unit for calculating the standard deviation delta of the point cloud2；
A threshold calculation unit for calculating a threshold T:
T＝μ+β×δ2
and the point cloud deleting unit is used for traversing the point cloud and deleting all the points with the average distance to the k adjacent points thereof being more than T.
In particular, the search radius ri：
ri＝diX α x β, where α is the angular resolution in the horizontal direction of the laser and β is a dynamic parameter greater than 1.
In a third aspect, another embodiment of the present invention discloses a patrol robot, which includes a driving unit, a power supply, a processor, and a memory, wherein the memory stores instructions, and the instructions are executed by the processor to implement the three-dimensional laser noise reduction method.
The invention calculates the search radius r through the distance of each point in the laser coordinate systemiIn addition, the invention also calculates the standard deviation through the mean value of the adjacent points of the point cloud and the mean value of the whole point cloud, and obtains the threshold value of the point cloud to be deleted according to the mean value of the adjacent points of the point cloud, the mean value of the whole point cloud and the standard deviation, thereby better removing the noise.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of a three-dimensional laser denoising method according to an embodiment of the present invention;
FIG. 2 is an original view of a snowy laser provided by an embodiment of the invention;
FIG. 3 is a cloud point image after noise removal according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a comparison of structured information provided by an embodiment of the present invention;
fig. 5 is a schematic diagram of a three-dimensional laser noise reduction device according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of an outlier rejection unit according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a three-dimensional laser noise reduction device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention.
Example one
For an outdoor robot, the present embodiment has two preconditions:
a) the outdoor robot has obvious scene characteristics, and has more vehicles, trees, buildings and the like;
b) the rain and snow noise of the outdoor robot is a scattered area which is scattered outwards by taking the machine body coordinate system as the center and is irregular.
Wherein, in the assumption a, the outdoor robot is generally operated outdoors, which generally has obvious buildings, vehicles or trees. For hypothesis a, each key frame swept by the laser is characterized. The embodiment makes full use of the characteristic of each key frame of the laser frame, so that the globally unique description corresponding to each key frame has a unique identifier, the robustness is improved, and singular values are reduced; for hypothesis b, this is a weak hypothesis that all of the noise caused by rain and snow satisfies.
Referring to fig. 1, the present embodiment discloses a three-dimensional laser noise reduction method, which includes the following steps:
s1, acquiring a 3D laser point cloud image;
specifically, this step can receive 3D laser point cloud data through a callback function, and the 3D laser point cloud data includes: x-direction coordinates, y-direction coordinates, z-direction coordinates.
S2, down-sampling the 3D laser point cloud data to obtain down-sampled 3D laser point cloud data;
after the 3D laser point cloud is received, a great number of laser points exist in the point cloud. The number of points can seriously affect the time complexity of noise reduction, and in order to reduce the calculation amount and ensure the integrity of data, voxel down-sampling is used.
In the specific step, 0.1m cubes are respectively used for down-sampling in the directions of x, y and z in the 3D laser point cloud.
S3, converting the down-sampled 3D laser point cloud data into a kd tree;
the Kd tree is a tree-like data structure that converts all points in space into one tree, with each leaf node in the tree being a binary tree of k-dimensional points and the non-leaf nodes being treated as a hyperplane.
First, the variance of each axis is calculated, and the axis with the largest variance is divided. Finding the median of the corresponding coordinate of the current axis on the divided axis, wherein the point corresponding to the median is the root node. The left sub-tree of the root node is a point smaller than the current corresponding dimension, and the right sub-tree is a point larger than the current dimension. And next, segmenting on a secondary axis, circulating the steps, and finally converting the complete laser point cloud into a kd tree. The conversion into the kd-tree is for the following nearest neighbor search.
S4, calculating the distance of each point in the down-sampled 3D laser point cloud under a laser coordinate system;
if the point set of the laser point cloud is marked as P and coexists at n points, then P ═ P1,p2,p3,...,pnH, and pi＝(xi,yi,zi). The distance d of each point to the laser coordinate systemiComprises the following steps:
s5, calculating the search radius r according to the distance of each point in the laser coordinate systemi；
According to the calculated distance d of each pointiCalculating a search radius r of each pointi：
ri＝di×α×β
Where α is the angular resolution in the horizontal direction of the laser and β is a dynamic parameter greater than 1.
S6, calculating the nearest neighbor of each point in the point cloud according to the search radius, searching whether the current sphere meets the threshold value of the minimum point number, if so, retaining the point, otherwise, rejecting the point;
searching radius r by taking the current point as a centeriAnd searching whether the threshold value of the minimum point number is met in the current sphere. If the current point is satisfied, the point is retained, otherwise the point is rejected.
S7, according to the point cloud piMean value mu of neighboring points ofiObtaining the point cloud standard deviation delta from the mean value mu of the whole point cloud2And according to said mean value muiMean value mu of the entire point cloud, point cloud standard deviation delta2Obtaining a threshold value T, deleting and the point cloud piPoints with an average distance from their close points greater than T.
After the operations are performed on each point in the point cloud, most noise points such as rain, snow and the like are completely removed, but due to the existence of errors, a part of noise points may not be completely removed, and finally, static outliers are added for removal.
First calculate point piMean value mu of nearest k neighboring point distancesi：
Assuming that the point cloud follows Gaussian distribution, the mean value mu calculated in the previous stepiCalculating the mean value mu of the whole point cloud:
calculating the standard deviation delta of the point cloud2：
Calculating a threshold value:
T＝μ+β×δ2
and finally traversing the point cloud, and deleting all points with the average distance to the k adjacent points thereof being more than T.
Referring to fig. 2-4, wherein fig. 2 is a snowy laser raw image with noise, the scattered points in fig. 2 are noise; FIG. 3 is a point cloud image after noise rejection; fig. 4 is a structural information comparison, wherein white is a point cloud image after denoising, and red is an original point cloud image. As can be seen from fig. 2 to 4, the three-dimensional laser noise reduction method of the present embodiment can effectively remove noise in snowy weather. The three-dimensional laser noise reduction method can eliminate more than 98% of noise points generated by rain, snow and the like on the laser.
The embodiment calculates the search radius r by the distance of each point in the laser coordinate systemiCompared with the manual setting of the search radius in the prior art, the automatic setting of the search radius can be realized, the accuracy is higher, in addition, the standard deviation is calculated through the mean value of the adjacent points of the point cloud and the mean value of the whole point cloud, and the threshold value of the point cloud needing to be deleted is obtained according to the mean value of the adjacent points of the point cloud, the mean value of the whole point cloud and the standard deviation, so that the noise is better removed.
Example two
Referring to fig. 5, the present embodiment discloses a three-dimensional laser noise reduction apparatus, which includes the following units:
the point cloud obtaining unit is used for obtaining a 3D laser point cloud image;
specifically, the point cloud obtaining unit may receive the 3D laser point cloud data through a callback function, where the 3D laser point cloud data includes: x-direction coordinates, y-direction coordinates, z-direction coordinates.
The down-sampling unit is used for down-sampling the 3D laser point cloud data to acquire the down-sampled 3D laser point cloud data;
after the 3D laser point cloud is received, a great number of laser points exist in the point cloud. The number of points can seriously affect the time complexity of noise reduction, and in order to reduce the calculation amount and ensure the integrity of data, voxel down-sampling is used.
The down-sampling unit is used for down-sampling the 3D laser point cloud in the x direction, the y direction and the z direction by using a cube of 0.1 m.
A Kd tree generation unit that converts the downsampled 3D laser point cloud data into a Kd tree;
the Kd tree is a tree-like data structure that converts all points in space into one tree, with each leaf node in the tree being a binary tree of k-dimensional points and the non-leaf nodes being treated as a hyperplane.
First, the variance of each axis is calculated, and the axis with the largest variance is divided. Finding the median of the corresponding coordinate of the current axis on the divided axis, wherein the point corresponding to the median is the root node. The left sub-tree of the root node is a point smaller than the current corresponding dimension, and the right sub-tree is a point larger than the current dimension. And next, segmenting on a secondary axis, circulating the steps, and finally converting the complete laser point cloud into a kd tree. The conversion into the kd-tree is for the following nearest neighbor search.
The distance calculation unit is used for calculating the distance of each point in the down-sampled 3D laser point cloud under a laser coordinate system;
if the point set of the laser point cloud is marked as P and coexists at n points, then P ═ P1,p2,p3,...,pnAnd p isi＝(xi,yi,zi). The distance d of each point to the laser coordinate systemiComprises the following steps:
a search radius calculation unit for calculating a search radius r according to the distance of each point in the laser coordinate systemi；
According to the calculated distance d of each pointiCalculating a search radius r of each pointi：
ri＝di×α×β
Where α is the angular resolution in the horizontal direction of the laser and β is a dynamic parameter greater than 1.
The noise removing unit is used for calculating the nearest neighbor of each point in the point cloud according to the searching radius, searching whether the threshold value of the minimum point number is met in the current sphere or not, if the current point is met, keeping the point, and if not, rejecting the point;
searching for radius r with the current point as the centeriAnd searching whether the threshold value of the minimum point number is met in the current sphere. If the current point is satisfied, the point is retained, otherwise the point is rejected.
An outlier rejection unit for rejecting the point cloud piMean value mu of neighboring points ofiObtaining the point cloud standard deviation delta from the mean value mu of the whole point cloud2And according to said mean value muiMean value mu of the entire point cloud, point cloud standard deviation delta2Obtaining a threshold value T, deleting and the point cloud piPoints with an average distance from their close points greater than T.
After the operations are performed on each point in the point cloud, most noise points such as rain, snow and the like are completely removed, but due to the existence of errors, a part of noise points may not be completely removed, and finally, static outliers are added for removal.
Referring to fig. 6, the outlier culling unit further comprises:
a point cloud mean calculation unit for calculating a point piMean value mu of nearest k neighboring point distancesi：
A whole point cloud mean calculation unit for calculating the mean mu according to the point cloud meaniCalculating the mean value mu of the whole point cloud;
assuming that the point cloud follows Gaussian distribution, the mean value mu calculated in the previous stepiCalculating the mean value mu of the whole point cloud:
a point cloud standard deviation calculating unit for calculating the standard deviation delta of the point cloud2：
A threshold calculation unit for calculating a threshold:
T＝μ+β×δ2
and the point cloud deleting unit is used for finally traversing the point cloud and deleting all the points with the average distance to the k adjacent points thereof being more than T.
Referring to fig. 2-4, wherein fig. 2 is a snowy laser raw image with noise, the scattered points in fig. 2 are noise; FIG. 3 is a point cloud image after noise rejection; fig. 4 is a structural information comparison, where white is a point cloud image after denoising and red is an original point cloud image. As can be seen from fig. 2 to 4, the three-dimensional laser noise reduction method of the present embodiment can effectively remove noise in snowy weather. The three-dimensional laser noise reduction method can eliminate more than 98% of noise points generated by rain, snow and the like on the laser.
In addition, the embodiment also calculates the standard deviation through the mean value of the adjacent points of the point cloud and the mean value of the whole point cloud, and obtains the threshold value of the point cloud to be deleted according to the mean value of the adjacent points of the point cloud, the mean value of the whole point cloud and the standard deviation, thereby better removing noise.
EXAMPLE III
Referring to fig. 7, fig. 7 is a schematic structural diagram of a three-dimensional laser noise reduction device of the present embodiment. The three-dimensional laser noise reduction device 20 of this embodiment includes a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. The processor 21 realizes the steps in the above-described method embodiments when executing the computer program. Alternatively, the processor 21 implements the functions of the modules/units in the above-described device embodiments when executing the computer program.
Illustratively, the computer program may be divided into one or more modules/units, which are stored in the memory 22 and executed by the processor 21 to accomplish the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the three-dimensional laser noise reduction device 20. For example, the computer program may be divided into the modules in the second embodiment, and for the specific functions of the modules, reference is made to the working process of the apparatus in the foregoing embodiment, which is not described herein again.
The three-dimensional laser noise reduction device 20 may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will appreciate that the schematic diagram is merely an example of the three-dimensional laser noise reduction apparatus 20 and does not constitute a limitation of the three-dimensional laser noise reduction apparatus 20 and may include more or fewer components than those shown, or some components may be combined, or different components, for example, the three-dimensional laser noise reduction apparatus 20 may also include an input-output device, a network access device, a bus, etc.
The Processor 21 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The general processor may be a microprocessor or the processor may be any conventional processor, etc., and the processor 21 is a control center of the three-dimensional laser noise reduction device 20 and connects the various parts of the entire three-dimensional laser noise reduction device 20 by using various interfaces and lines.
The memory 22 may be used to store the computer program and/or module, and the processor 21 implements various functions of the three-dimensional laser noise reduction device 20 by running or executing the computer program and/or module stored in the memory 22 and calling up data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory 22 may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Wherein, the integrated module/unit of the three-dimensional laser noise reduction device 20 can be stored in a computer readable storage medium if it is implemented in the form of software functional unit and sold or used as a stand-alone product. Based on such understanding, all or part of the flow of the method according to the above embodiments may be implemented by a computer program, which may be stored in a computer readable storage medium and used by the processor 21 to implement the steps of the above embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection therebetween, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
Example four
The embodiment discloses a patrol robot, which comprises a driving unit, a power supply, a processor and a memory, wherein the memory stores instructions, and the instructions are used for realizing the three-dimensional laser noise reduction method when being executed by the processor.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A three-dimensional laser noise reduction method comprises the following steps:
s1, acquiring a 3D laser point cloud image;
s2, down-sampling the 3D laser point cloud data to obtain down-sampled 3D laser point cloud data;
s3, converting the down-sampled 3D laser point cloud data into a kd tree;
s4, calculating the distance of each point in the down-sampled 3D laser point cloud under a laser coordinate system;
s5, calculating the search radius r according to the distance of each point in the laser coordinate systemi；
S6, calculating the nearest neighbor of each point in the point cloud according to the dynamic radius, searching whether the current sphere meets the threshold value of the minimum point number, if so, keeping the point, otherwise, removing the point.
2. The method of claim 1, further comprising the steps of:
s7, according to the point cloud piMean value mu of neighboring points ofiObtaining the point cloud standard deviation delta from the mean value mu of the whole point cloud2And according to said mean value muiMean value mu of the entire point cloud, point cloud standard deviation delta2Obtaining a threshold value T, deleting and the point cloud piPoints with an average distance from their close points greater than T.
3. The method according to claim 2, wherein the step S7 specifically includes:
s71, calculating a point piMean value mu of nearest k neighboring point distancesi；
S72, according to the mean value muiCalculating the mean value mu of the whole point cloud;
s73, calculating the standard deviation delta of the point cloud2；
S74, calculating threshold T:
T＝μ+β×δ2
and S75, traversing the point cloud, and deleting all points with the average distance to the k adjacent points thereof being more than T.
4. The method of claim 3, the search radius ri：
ri＝diX α x β, where α is the angular resolution in the horizontal direction of the laser and β is a dynamic parameter greater than 1.
5. The method of claim 4, the downsampling being voxel downsampling.
6. A three-dimensional laser noise reduction device comprises the following units:
the point cloud obtaining unit is used for obtaining a 3D laser point cloud image;
the down-sampling unit is used for down-sampling the 3D laser point cloud data to acquire the down-sampled 3D laser point cloud data;
a Kd tree generation unit for converting the downsampled 3D laser point cloud data into a Kd tree;
the distance calculation unit is used for calculating the distance of each point in the down-sampled 3D laser point cloud under a laser coordinate system;
a search radius calculation unit for calculating a search radius r according to the distance of each point in the laser coordinate systemi；
And the noise removing unit is used for calculating the nearest neighbor of each point in the point cloud according to the dynamic radius, searching whether the threshold value of the minimum point number is met in the current sphere or not, if the current point is met, keeping the point, and if not, rejecting the point.
7. The apparatus of claim 6, further comprising the following:
an outlier rejection unit for rejecting the point cloud piMean value mu of neighboring points ofiObtaining the point cloud standard deviation delta from the mean value mu of the whole point cloud2And according to said mean value muiMean value mu of the entire point cloud, point cloud standard deviation delta2Obtaining a threshold value T, deleting and the point cloud piPoints with an average distance from their close points greater than T.
8. The apparatus of claim 7, the outlier culling unit further comprising:
a point cloud mean calculating unit for calculating a point piMean value mu of nearest k neighboring point distancesi；
A whole point cloud mean calculation unit for calculating the mean mu according to the point cloud meaniCalculating the mean value mu of the whole point cloud;
a point cloud standard deviation calculating unit for calculating the standard deviation delta of the point cloud2；
A threshold calculation unit for calculating a threshold T:
T＝μ+β×δ2
and the point cloud deleting unit is used for traversing the point cloud and deleting all the points with the average distance to the k adjacent points thereof being more than T.
9. The apparatus of claim 8, theSearch radius ri：
ri＝diX α x β, where α is the angular resolution in the horizontal direction of the laser and β is a dynamic parameter greater than 1.
10. A patrol robot comprising a drive unit, a power supply, and a processor, a memory having stored thereon instructions, which when executed by the processor, are adapted to implement the three-dimensional laser noise reduction method of any of claims 1-5.
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| CN115436912A (en) * | 2022-11-02 | 2022-12-06 | 苏州一径科技有限公司 | Point cloud processing method and device and laser radar |
| CN117191781A (en) * | 2023-04-20 | 2023-12-08 | 成都飞机工业（集团）有限责任公司 | Nondestructive testing system and method for micro array hole through hole rate of composite wallboard |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115436912A (en) * | 2022-11-02 | 2022-12-06 | 苏州一径科技有限公司 | Point cloud processing method and device and laser radar |
| CN117191781A (en) * | 2023-04-20 | 2023-12-08 | 成都飞机工业（集团）有限责任公司 | Nondestructive testing system and method for micro array hole through hole rate of composite wallboard |
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