CN117789198A - Method for realizing point cloud degradation detection based on 4D millimeter wave imaging radar - Google Patents
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Abstract
The invention relates to a point cloud degradation detection method for realizing point cloud information entropy fusion point cloud shape based on a 4D millimeter wave imaging radar, which comprises the following steps: inputting 4D millimeter wave Lei Dadian cloud, performing point cloud pretreatment on the cloud, and removing outlier noise points; calculating the information entropy of the point cloud, judging whether the currently acquired point cloud is degraded, if so, performing further fitting processing on the shape of the current point cloud, and judging whether the shape tends to be regular, if so, degrading the point cloud, otherwise, not degrading the current point cloud. The invention also relates to a corresponding device, processor and computer readable storage medium thereof. The method, the device, the processor and the computer readable storage medium for detecting the point cloud degradation based on the 4D millimeter wave imaging radar realize the point cloud information entropy fusion point cloud shape, calculate the information entropy directly from the self space distribution condition of the point cloud, judge whether the point cloud is degraded according to the information entropy, and further improve the accuracy of degradation detection.
Description
Technical Field
The invention relates to the field of 4D millimeter wave imaging radars, in particular to the technical field of point cloud degradation detection, and specifically relates to a point cloud degradation detection method, a device, a processor and a computer readable storage medium for realizing point cloud information entropy fusion point cloud shape based on a 4D millimeter wave imaging radar.
Background
In the SLAM technical field, point cloud is a very common input, and point cloud matching is an important module for SLAM front-end processing. The point cloud is typically generated by a lidar or a 4D millimeter wave radar. For some environments similar, in a scene with weak textures and no textures, the distribution conditions of point clouds tend to be consistent, and basically, different points cannot be proposed for feature matching, so that an incorrect matching result is caused, and the post precision of an automatic driving vehicle estimated by the SLAM front end is very low, so that the reconstruction of environmental information cannot be completed. The point cloud in this scenario is the degenerate point cloud.
At present, aiming at SLAM mapping under similar scenes, whether the current point cloud is degenerated is generally judged from the result of point cloud matching, and the degeneracy direction of a certain dimension is calculated, for example, based on the score of point cloud matching, the ratio of the maximum eigenvalue to the minimum eigenvalue of a Hessian matrix in the matching solving process and the average error value after matching. Although the method can detect the degenerated point cloud to a certain extent, the method is mainly used for laser radar point cloud, has poor applicability for 4D millimeter wave Lei Dadian cloud, is not robust because 4D millimeter wave radar point cloud points are very sparse and have larger noise, and can not detect non-degenerated point cloud or detect normal point cloud as degenerated point cloud under different degenerated scenes because different threshold parameters are required to be set.
Therefore, it is necessary to propose a scheme capable of simultaneously overcoming the defect of poor robustness of the existing laser radar point cloud degradation detection method and sparse 4D millimeter wave imaging radar point cloud, so as to improve the applicability of the 4D millimeter wave radar point cloud degradation detection.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a point cloud degradation detection method, a device, a processor and a computer readable storage medium thereof for realizing point cloud information entropy fusion point cloud shape based on a 4D millimeter wave imaging radar.
In order to achieve the above object, the method, the device, the processor and the computer readable storage medium for detecting the degradation of the point cloud based on the 4D millimeter wave imaging radar to realize the entropy fusion of the point cloud information and the shape of the point cloud according to the present invention are as follows:
the method for detecting the point cloud degradation based on the 4D millimeter wave imaging radar to realize the point cloud information entropy fusion point cloud shape is mainly characterized by comprising the following steps:
(1) Inputting 4D millimeter wave Lei Dadian cloud, performing point cloud pretreatment on the cloud, and removing outlier noise points;
(2) Calculating point cloud information entropy, judging whether the point cloud acquired at present is degenerated or not, and if so, entering a step (3);
(3) And performing further fitting processing on the current point cloud shape, judging whether the shape tends to be regular, if so, degrading the point cloud, otherwise, not degrading the current point cloud.
Preferably, the step (1) specifically includes the following steps:
(1.1) inputting the 4D millimeter wave Lei Dadian cloud at the current momentN is the number of input point clouds;
(1.2) cloud to 4D millimeter wave Lei DadianIs +.>，/>Searching N neighbors around its radius R, if N satisfies the following condition, then it is stated +.>Is a noise point:
；
wherein alpha is a set threshold value of the number of the neighboring points;
(1.3) if PointIs noise point, will be->From 4D millimeter wave Lei Dadian cloud->Deleting to obtain the preprocessed point cloud +.>M is the number of the point clouds after pretreatment.
Preferably, the step (2) specifically includes the following steps:
(2.1) calculating a Point cloud center Point in the following manner：
；
Wherein m is point cloudThe number of point clouds in->For->Coordinates of (c);
(2.2) calculating the Point cloud covariance as follows：
；
(2.3) calculating the Point cloud information entropy in the following manner：
；
Wherein e is natural logarithm;
(2.4) determining the point cloud in the following mannerWhether degradation occurs:
；
wherein,entropy threshold for point cloud degradation information;
(2.5) if a point cloudAnd (3) if the degradation occurs, entering a step, otherwise, not degrading the current point cloud, and exiting degradation detection.
Preferably, the step (3) specifically includes the following steps:
(3.1) performing point cloud region of interest (ROI) extraction processing: at the point cloudSelecting x, y and z directions +.>Points within the range, get the point cloud +.>T is the number of point clouds after extracting the ROI, i.e. +.>；
(3.2) Point cloudExtracting the outline and obtaining boundary points of the point cloud outline;
(3.3) detecting gradient change of the boundary point cloud based on the obtained point cloud outline boundary points, and finding out the vertex of the current point cloud shape according to the gradient change;
and (3.4) connecting the obtained t vertexes in pairs in sequence to form t-1 vectors, judging whether the t-1 vectors are on the same plane, if not, the current point cloud is not in a regular shape, and if not, the point cloud is not degraded, otherwise, the point cloud is degraded.
Preferably, the step (3.2) specifically includes the following steps:
(3.2.1) computing Point cloudIs>Normal of->：
(3.2.2) calculation pointsNearby N neighbor points->Normal vector->；
(3.2.3) judging whether the included angle theta meets the following conditions:
；
(3.2.4) obtaining boundary points of the point cloud contour through the contour extractionThe number of boundary points.
Preferably, the step (3.3) specifically includes the following steps:
(3.3.1) calculating a contour point gradient in the following manner：
；
；
Wherein,is boundary point->Coordinate points of +.>Is boundary point->And->A distance therebetween;
(3.3.2) gradient variation difference was calculated as follows：
；
Wherein,the previous contour point gradient;
(3.3.3) computing Point cloud shape verticesThe boundary point is considered to be present if the following condition is satisfied>Is a point cloud shape vertex->：
；
Wherein,is a gradient change threshold.
Preferably, the step (3.4) specifically includes the following steps:
(3.4.1) acquiring a point cloud shape vector: vertex pointAnd->Component vector->Vertex->And->Component vector->Obtaining the shape vector of the vertex +.>；
(3.4.2) computing vectorsAnd shape vector->Plane equation determined between other vectors of (a)>Wherein->Parameters of plane equation are determined;
(3.4.3) determining whether all plane equations satisfy the following condition:
；
wherein t represents the number of vertices of the point cloud shape.
(3.4.4) if all planes meet the above condition, indicating that all vectors are in the same plane, the shape of the point cloud tends to be regular and the point cloud is degraded currently, otherwise, the point cloud is not degraded.
The point cloud degradation detection device for realizing the point cloud information entropy fusion point cloud shape based on the 4D millimeter wave imaging radar comprises:
a processor configured to execute computer-executable instructions;
and the memory stores one or more computer executable instructions which, when executed by the processor, realize the step of the point cloud degradation detection method for realizing the point cloud information entropy fusion point cloud shape based on the 4D millimeter wave imaging radar.
The point cloud degradation detection processor based on the 4D millimeter wave imaging radar realizes the point cloud information entropy fusion point cloud shape, wherein the processor is configured to execute computer executable instructions, and when the computer executable instructions are executed by the processor, the step of the point cloud degradation detection method based on the 4D millimeter wave imaging radar realizes the point cloud information entropy fusion point cloud shape is realized.
The computer readable storage medium has a computer program stored thereon, the computer program being executable by a processor to implement the above-described method for detecting the degradation of a point cloud based on the 4D millimeter wave imaging radar to realize the entropy fusion point cloud shape of the point cloud information.
The point cloud degradation detection method, the device, the processor and the computer readable storage medium thereof for realizing the point cloud information entropy fusion point cloud shape based on the 4D millimeter wave imaging radar solve the problems of limitation and false detection that laser radar point cloud degradation detection can only be judged by a front-back frame matching result, and judge whether the point cloud really degrades or not directly from the spatial position distribution inside the point cloud, the relativity between the points and the shape rule of the point cloud.
Drawings
Fig. 1 is a flowchart of a point cloud degradation detection method for realizing a point cloud information entropy fusion point cloud shape based on a 4D millimeter wave imaging radar.
Detailed Description
In order to more clearly describe the technical contents of the present invention, a further description will be made below in connection with specific embodiments.
Before describing in detail embodiments that are in accordance with the present invention, it should be observed that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Before explaining the technical scheme in detail, the following explanation is made on some technical features:
SLAM: building and positioning in real time;
pose: a position pose including position information, x, y, z and pose information, roll, pitch, yaw;
ROI: region of Interesting, a region of interest.
Referring to fig. 1, the method for detecting the point cloud degradation based on the 4D millimeter wave imaging radar to realize the point cloud information entropy fusion point cloud shape includes the following steps:
(1) Inputting 4D millimeter wave Lei Dadian cloud, performing point cloud pretreatment on the cloud, and removing outlier noise points;
(2) Calculating point cloud information entropy, judging whether the point cloud acquired at present is degenerated or not, and if so, entering a step (3);
(3) And performing further fitting processing on the current point cloud shape, judging whether the shape tends to be regular, if so, degrading the point cloud, otherwise, not degrading the current point cloud.
As a preferred embodiment of the present invention, the step (1) specifically includes the steps of:
(1.1) inputting the 4D millimeter wave Lei Dadian cloud at the current momentN is the number of input point clouds;
(1.2) cloud to 4D millimeter wave Lei DadianIs +.>，/>Searching N adjacent points near the radius R, ifSatisfying the following condition indicates->Is a noise point:
；
wherein alpha is a set threshold value of the number of the neighboring points;
(1.3) if PointIs noise point, will be->From 4D millimeter wave Lei Dadian cloud->Deleting to obtain the preprocessed point cloud +.>M is the number of the point clouds after pretreatment.
As a preferred embodiment of the present invention, the step (2) specifically includes the following steps:
(2.1) calculating a Point cloud center Point in the following manner：
；
Wherein m is point cloudThe number of point clouds in->For->Coordinates of (c);
(2.2) according to the followingComputing point cloud covariance：
；
(2.3) calculating the Point cloud information entropy in the following manner：
；
Wherein e is natural logarithm;
(2.4) determining the point cloud in the following mannerWhether degradation occurs:
；
wherein,entropy threshold for point cloud degradation information;
(2.5) if a point cloudAnd (3) if the degradation occurs, entering a step, otherwise, not degrading the current point cloud, and exiting degradation detection.
As a preferred embodiment of the present invention, the step (3) specifically includes the following steps:
(3.1) performing point cloud region of interest (ROI) extraction processing: at the point cloudSelecting x, y and z directions +.>Within the range ofPoint, get the point cloud->T is the number of point clouds after extracting the ROI, i.e
；
(3.2) Point cloudExtracting the outline and obtaining boundary points of the point cloud outline;
(3.3) detecting gradient change of the boundary point cloud based on the obtained point cloud outline boundary points, and finding out the vertex of the current point cloud shape according to the gradient change;
and (3.4) connecting the obtained t vertexes in pairs in sequence to form t-1 vectors, judging whether the t-1 vectors are on the same plane, if not, the current point cloud is not in a regular shape, and if not, the point cloud is not degraded, otherwise, the point cloud is degraded.
As a preferred embodiment of the present invention, the step (3.2) specifically includes the steps of:
(3.2.1) computing Point cloudIs>Normal of->：
(3.2.2) calculation pointsNearby N neighbor points->Normal vector；
(3.2.3) judging whether the included angle theta meets the following conditions:
；
(3.2.4) obtaining boundary points of the point cloud contour through the contour extractionThe number of boundary points.
As a preferred embodiment of the present invention, the step (3.3) specifically includes the steps of:
(3.3.1) calculating a contour point gradient in the following manner：
；
；
Wherein,is boundary point->Coordinate points of +.>Is boundary point->And->A distance therebetween;
(3.3.2) gradient variation difference was calculated as follows：
；
Wherein,the previous contour point gradient;
(3.3.3) computing Point cloud shape verticesThe boundary point is considered to be present if the following condition is satisfied>Is a point cloud shape vertex->：
；
Wherein,is a gradient change threshold.
As a preferred embodiment of the present invention, the step (3.4) specifically includes the steps of:
(3.4.1) acquiring a point cloud shape vector: vertex pointAnd->Component vector->Vertex->And->Component vector->Obtaining the shape vector of the vertex +.>；
(3.4.2) computing vectorsAnd shape vector->Plane equation determined between other vectors of (a)>Wherein->Parameters of plane equation are determined;
(3.4.3) determining whether all plane equations satisfy the following condition:
；
wherein t represents the number of vertices of the point cloud shape.
(3.4.4) if all planes meet the above condition, indicating that all vectors are in the same plane, the shape of the point cloud tends to be regular and the point cloud is degraded currently, otherwise, the point cloud is not degraded.
Referring to fig. 1, the method for detecting the point cloud degradation based on the 4D millimeter wave imaging radar to realize the point cloud information entropy fusion point cloud shape specifically includes three processing modules of point cloud preprocessing, point cloud information entropy and point cloud shape, and the specific processing flow among the modules is as follows:
(1) Point cloud preprocessing
Because the 4D millimeter wave Lei Dadian cloud is large in sparsity and noise error, the Lei Dadian cloud is subjected to filtering and denoising pretreatment before the point cloud degradation detection, and some outlier noise points are removed. The specific method comprises the following steps:
inputting 4D millimeter wave Lei Dadian cloud at current momentN is the number of input point clouds;
for a pair ofIs +.>，/>Searching N adjacent points near the radius R;
description is made if N satisfies the following conditionIs a noise point:
；
alpha is the threshold value of the number of the neighbor points, ifIs noise point, will be->From->Deleting to obtain the preprocessed point cloud +.>M is the number of the point clouds after pretreatment.
(2) Point cloud information entropy
The point cloud information entropy is used for explaining the distribution correlation of the internal space of the point cloud. The method comprises the steps of calculating a point cloud center, calculating a point cloud covariance, calculating a point cloud information entropy, and judging whether 4 processes are degraded or not:
(1) computing a point cloud center pointThe following formula:
；
m is point cloudPoint cloud number->For->Is defined by the coordinates of (a).
(2) Computing point cloud covariance：
；
(3) Calculating point cloud information entropy：
；
e is the natural logarithm.
(4) Point cloud degradation determination
If it isIf the following condition is satisfied, the point cloud is described>And if not, entering the point cloud shape module to further judge whether the point cloud really degenerates.
；
Entropy threshold for point cloud degradation information.
(3) Point cloud shape
Point cloud information entropyThe point cloud is larger in degradation probability, and whether the point cloud really degrades is further judged by fitting out whether the shape of the point cloud is regular or not. The method comprises 4 processes of point cloud ROI extraction, point cloud contour extraction, point cloud shape fitting and point cloud shape trend rule judgment.
(3.1) Point cloud ROI extraction
To efficiently fit the approximate shape of the point cloud, the point cloud is formed byIs selected from x, y and z directions based on (a)Points within the range, get the point cloud +.>T is the number of point clouds after extracting the ROI, i.e
；
(3.2) Point cloud contour extraction
At the point cloudExtracting the outline of the whole body +.>S is the number of contour points, and the specific flow is as follows:
a) Calculate each pointNormal of->The normal is calculated as the most basic processing method in space geometry;
b) Calculation ofNearby N neighbor points->Normal vectorThe normal vector is calculated as a space geometric basic processing method;
c) If the angle theta satisfies the following condition,is a contour point
；
Obtaining boundary points of the point cloud contour through contour extractionThe number of boundary points.
(3.3) Point cloud shape fitting
Utilizing the contour points extracted in (3.2) to fit the shape of the point cloud, namely detecting gradient change of the contour point cloud, and finding out the vertex of the shape according to the gradient changeThe specific process flow is as follows:
a) Calculating contour pointsIs a gradient of (2);
；
；
is->Coordinate points of +.>Is->And->Distance between them.
b) Calculating gradient change difference：
；
c) Computing shape verticesIf the following conditions are met then consider +.>Is the vertex of the shape->：
；
Is a gradient change threshold.
(3.4) Point cloud shape trending rule judgment
In (3.3), the vertices of the point cloud shape are obtainedAnd (3) connecting the t vertexes in pairs in sequence to form t-1 vectors, checking whether the t-1 vectors are on the same plane, if not, carrying out regular shape, and if not, carrying out degradation on the point cloud, otherwise, carrying out degradation. The specific process is as follows:
a) Vertex pointAnd->Component vector->Vertex->And->Component vector->Obtaining the shape vector of the vertex +.>；
b) Calculation ofAnd->Plane equations determined between other vectors in (a)，/>Is a determined parameter of a plane equation, and the plane equation is a basic processing method in geometry.
c) If all plane equations are satisfied:
；
the description is the same plane, the shape of the point cloud tends to be regular, the point cloud is degraded, and otherwise, the point cloud is not degraded.
The point cloud degradation detection device for realizing the point cloud information entropy fusion point cloud shape based on the 4D millimeter wave imaging radar comprises:
a processor configured to execute computer-executable instructions;
and the memory stores one or more computer executable instructions which, when executed by the processor, realize the step of the point cloud degradation detection method for realizing the point cloud information entropy fusion point cloud shape based on the 4D millimeter wave imaging radar.
The point cloud degradation detection processor based on the 4D millimeter wave imaging radar realizes the point cloud information entropy fusion point cloud shape, wherein the processor is configured to execute computer executable instructions, and when the computer executable instructions are executed by the processor, the step of the point cloud degradation detection method based on the 4D millimeter wave imaging radar realizes the point cloud information entropy fusion point cloud shape is realized.
The computer readable storage medium has a computer program stored thereon, the computer program being executable by a processor to implement the above-described method for detecting the degradation of a point cloud based on the 4D millimeter wave imaging radar to realize the entropy fusion point cloud shape of the point cloud information.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution device.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, and the program may be stored in a computer readable storage medium, where the program when executed includes one or a combination of the steps of the method embodiments.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, reference to the terms "one embodiment," "some embodiments," "examples," "specific examples," or "embodiments," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
The point cloud degradation detection method, the device, the processor and the computer readable storage medium thereof for realizing the point cloud information entropy fusion point cloud shape based on the 4D millimeter wave imaging radar solve the problems of limitation and false detection that laser radar point cloud degradation detection can only be judged by a front-back frame matching result, and judge whether the point cloud really degrades or not directly from the spatial position distribution inside the point cloud, the relativity between the points and the shape rule of the point cloud.
In this specification, the invention has been described with reference to specific embodiments thereof. It will be apparent, however, that various modifications and changes may be made without departing from the spirit and scope of the invention. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.
Claims (10)
1. A point cloud degradation detection method for realizing point cloud information entropy fusion point cloud shape based on a 4D millimeter wave imaging radar is characterized by comprising the following steps:
(1) Inputting 4D millimeter wave Lei Dadian cloud, performing point cloud pretreatment on the cloud, and removing outlier noise points;
(2) Calculating point cloud information entropy, judging whether the point cloud acquired at present is degenerated or not, and if so, entering a step (3);
(3) And performing further fitting processing on the current point cloud shape, judging whether the point cloud shape tends to be regular, if so, degrading the point cloud, otherwise, not degrading the current point cloud.
2. The method for detecting the point cloud degradation based on the 4D millimeter wave imaging radar to realize the point cloud information entropy fusion point cloud shape according to claim 1, wherein the step (1) specifically comprises the following steps:
(1.1) inputting the 4D millimeter wave Lei Dadian cloud at the current momentN is the number of input point clouds;
(1.2) cloud to 4D millimeter wave Lei DadianIs +.>，/>N neighbors are searched for near their radius R, and the point +.>Is a noise point:
；
wherein alpha is a set threshold value of the number of the neighboring points;
(1.3) if PointFor noise point, then point +.>From 4D millimeter wave Lei Dadian cloud->Obtaining the first point cloud after pretreatment>M is the number of the point clouds after pretreatment.
3. The method for detecting the point cloud degradation based on the 4D millimeter wave imaging radar to realize the entropy fusion of the point cloud information and the point cloud shape according to claim 2, wherein the step (2) specifically comprises the following steps:
(2.1) calculating a Point cloud center Point in the following manner：
；
Wherein m is a first point cloudThe number of point clouds in->For->Coordinates of (c);
(2.2) calculating the Point cloud covariance as follows：
；
(2.3) calculating the Point cloud information entropy in the following manner：
；
Wherein e is natural logarithm;
(2.4) determining the first point cloud in the following mannerWhether degradation occurs:
；
wherein,entropy threshold for point cloud degradation information;
(2.5) if the first point cloudAnd (3) if the degradation occurs, entering a step, otherwise, not degrading the current point cloud, and exiting degradation detection.
4. The method for detecting the point cloud degradation based on the 4D millimeter wave imaging radar to realize the entropy fusion of the point cloud information and the point cloud shape according to claim 3, wherein the step (3) specifically comprises the following steps:
(3.1) performing point cloud region of interest (ROI) extraction processing: at a first point cloudSelecting x, y and z directions +.>Points within the range, obtaining a second point cloud +.>T is the number of point clouds after extracting the ROI, i.e
；
(3.2) for a second Point cloudExtracting the outline and obtaining boundary points of the point cloud outline;
(3.3) detecting gradient change of the boundary point cloud based on the obtained point cloud outline boundary points, and finding out the vertex of the current point cloud shape according to the gradient change;
and (3.4) connecting the obtained t vertexes in pairs in sequence to form t-1 vectors, judging whether the vectors are on the same plane, if not, the current point cloud is not in a regular shape, and if not, the point cloud is not degraded, otherwise, the point cloud is degraded.
5. The method for detecting the point cloud degradation based on the 4D millimeter wave imaging radar to realize the entropy fusion of the point cloud information and the point cloud shape according to claim 4, wherein the step (3.2) specifically comprises the following steps:
(3.2.1) computing a second Point cloudIs>Normal of->；
(3.2.2) calculation pointsNearby N neighbor points->Normal vector；
(3.2.3) judging whether the included angle theta meets the following conditions:
；
(3.2.4) obtaining boundary points of the point cloud contour through the contour extractionThe number of boundary points.
6. The method for detecting the point cloud degradation based on the 4D millimeter wave imaging radar to realize the entropy fusion of the point cloud information and the point cloud shape according to claim 5, wherein the step (3.3) specifically comprises the following steps:
(3.3.1) calculating a contour point gradient in the following manner：
；
；
Wherein,is boundary point->Coordinate points of +.>Is boundary point->And->A distance therebetween;
(3.3.2) gradient variation difference was calculated as follows：
；
Wherein,the previous contour point gradient;
（3.3.3) Computing point cloud shape verticesThe boundary point is considered to be present if the following condition is satisfied>Is a point cloud shape vertex->Wherein->：
；
Wherein,is a gradient change threshold.
7. The method for detecting the point cloud degradation based on the 4D millimeter wave imaging radar to realize the entropy fusion of the point cloud information and the point cloud shape according to claim 6, wherein the step (3.4) specifically comprises the following steps:
(3.4.1) acquiring a point cloud shape vector: vertex pointAnd->Component vector->Vertex->And->Composition vectorObtaining the shape vector of the vertex +.>；
(3.4.2) computing vectorsAnd shape vector->Plane equation determined between other vectors of (a)>Wherein->Parameters of plane equation are determined;
(3.4.3) determining whether all plane equations satisfy the following condition:
；
wherein t represents the number of the vertices of the point cloud shape;
(3.4.4) if all planes meet the above condition, indicating that all vectors are in the same plane, the shape of the point cloud tends to be regular and the point cloud is degraded currently, otherwise, the point cloud is not degraded.
8. A point cloud degradation detection device for realizing point cloud information entropy fusion point cloud shape based on a 4D millimeter wave imaging radar is characterized by comprising:
a processor configured to execute computer-executable instructions;
a memory storing one or more computer-executable instructions which, when executed by the processor, implement the steps of the method for detecting point cloud degradation for implementing a point cloud information entropy fusion point cloud shape based on a 4D millimeter wave imaging radar as claimed in any one of claims 1 to 7.
9. A point cloud degradation detection processor for realizing a point cloud information entropy fusion point cloud shape based on a 4D millimeter wave imaging radar, wherein the processor is configured to execute computer executable instructions, which when executed by the processor, realize the steps of the point cloud degradation detection method for realizing the point cloud information entropy fusion point cloud shape based on the 4D millimeter wave imaging radar according to any one of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon a computer program executable by a processor to implement the steps of the method for detecting point cloud degradation based on 4D millimeter wave imaging radar to realize point cloud information entropy fusion point cloud shape according to any one of claims 1 to 7.
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