CN113295142B - Terrain scanning analysis method and device based on FARO scanner and point cloud - Google Patents
Terrain scanning analysis method and device based on FARO scanner and point cloud Download PDFInfo
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
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- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract
The invention discloses a terrain scanning analysis method and a terrain scanning analysis device based on a FARO scanner and point cloud, which comprises the following steps: remotely controlling a FARO scanner to scan a complex scene to obtain scattered point cloud data; calculating a normal vector of the point cloud data by using a PCA algorithm, dividing the three-dimensional point cloud data by using an improved region growing method, and performing spherical fitting on a divided point cloud cluster set through sampling consistency to calculate spherical parameters; and according to the coordinates of the three spherical targets on the trolley and the coordinates of a new coordinate system established by the three spheres, solving the transformation relation between the new coordinate system and the coordinate system of the scanner to acquire the pose of the probe vehicle in the scene. By adopting the technical scheme of the invention, the three-dimensional coordinates of the center of the target sphere are quickly fitted by utilizing the characteristic that the spherical target has general regular symmetry and establishing a mathematical model and an improved recognition algorithm by means of a space spherical equation so as to solve the conversion of a point cloud coordinate system and finally realize the accurate positioning.
Description
Technical Field
The invention belongs to the technical field of three-dimensional point cloud data processing, and particularly relates to a terrain scanning analysis method and a terrain scanning analysis device based on a FARO scanner and point cloud.
Background
With the development of scientific technology, a novel spatial three-dimensional coordinate data acquisition technology, namely a three-dimensional laser scanning technology, plays an increasingly important role in measurement and spatial positioning work. The three-dimensional laser scanning technology is concerned by people because of the characteristics of high data acquisition speed, strong real-time performance, large data volume, high precision and the like. By utilizing the three-dimensional laser scanning technology, the full-automatic stepping scanning measurement with omnibearing, multi-angle and non-contact can be carried out on an object, the complete collection of various large and complex entity or real three-dimensional data can be realized, and further, the three-dimensional model of an entity target and various drawing data such as lines, surfaces, bodies, spaces and the like can be rapidly reconstructed.
However, in actual measurement work, problems such as limitation of a view angle of a scanner or shielding of a target object in a complex scene often occur, and collected scattered three-dimensional point cloud data is not ideal enough in imagination, so that certain influence is generated on precision measurement and next processing of the point cloud data. To address this problem, the prior art typically lays out artificial markers or directional targets in the scene that needs to be measured. The directional target comprises a plane reflection target and a spherical target, and the plane reflection target is required to be basically vertical to the scanning laser beam, so that the directional target is not suitable for multi-angle observation. In the prior art, a target ball is generally positioned by fitting through a least square method, and noise point cloud in a scene has certain influence on identification efficiency. The target is identified by determining a point cloud ring and a point cloud zone where the target is located, screening candidate point clouds containing the target, deleting repeated targets and false targets, and finally determining the true target. The technology has the advantages of automatic detection of multi-target balls and accurate precision, but has the defects of low overall efficiency, high instrument placement requirement, incapability of being used in a long distance and the like.
Disclosure of Invention
The invention aims to solve the technical problem of providing a terrain scanning analysis method and a terrain scanning analysis device based on a FARO scanner and point cloud.
In order to achieve the purpose, the invention adopts the following technical scheme:
a terrain scanning analysis method based on a FARO scanner and point cloud comprises the following steps:
step 2, calculating a normal vector of the point cloud data by utilizing a Principal Component Analysis (PCA) algorithm, segmenting the three-dimensional point cloud data by utilizing an improved region growing method, and performing spherical fitting on the segmented point cloud cluster set through sampling consistency to calculate spherical parameters;
and 3, solving the conversion relation between the new coordinate system and the coordinate system of the scanner to acquire the pose of the probe vehicle in the scene according to the coordinates of the three spherical targets on the trolley and the coordinates of the new coordinate system established by the three spheres.
Preferably, in the step 2, a PCA principal component analysis method is used for calculating a point cloud data normal vector, and calculation is performed through a locally fitted surface of an area where the point cloud is located, and the specific process comprises the following steps:
step 201, initializing an input point cloud object cloudFind;
step 202, initializing a Kdtree object search _ tree;
step 203, loading the closed _ find into a Kdtree for traversing query;
step 204, initializing normal estimation of an object;
step 205, setting normallestimation to search in a search _ tree mode;
step 206, setting a search Radius;
step 207, initializing an output normal data set object, closed _ with _ normals;
step 208, for each point p in the point cloud cluster:
(1) Searching nearest neighbor elements of the p point to obtain searchForNeighbors;
(2) Calculating the surface normal n of the point p by using a computePoint normal method;
(3) The normal direction is calculated by PCA, checking if the direction of n points consistently to the viewpoint, and if not, flipping using the setViewPoint () method.
Preferably, in step 206, the Radius =0.06 is searched.
Preferably, the point cloud segmentation specific process in step 2 is as follows:
step 209, setting k =20 of k neighbor search;
step 210, initializing an index k _ neighbor _ index of k neighbor domain search;
step 211, initializing a distance k _ neighbor _ index _ dis between index points searched by the k neighbor domain;
step 212, initializing a set object vec _ current for storing the curvature and the index of each point region position;
step 213, traversing the input point cloud object circle _ find, calculating the curvature of each point p and the corresponding index position by taking k, k _ neighbor _ index and k _ neighbor _ index _ dis as parameters, and storing the curvature and the corresponding index position in the vec _ current set;
step 214, sorting the curvatures in the vec _ current set from small to large by using a sort function;
step 215, setting a curvature threshold and a normal vector threshold;
step 216, setting the point with the minimum curvature as an initial seed point seed _ original;
step 217, traversing the seed queue seed, testing the included angle between each adjacent point and the normal vector of the seed point, and if the included angle is smaller than a critical value, adding the included angle into the current seed area; testing the curvature of each adjacent point, and if the curvature is smaller than a critical value, adding the point into a seed list of the current area; until all seed queues are judged;
step 218, storing the segmented results meeting the above conditions in a clusters;
step 219, traversing the cluster point set lists cluster, performing spherical surface fitting on each cluster by using a sampling consistency segmentation algorithm, and calculating the spherical center and radius parameters of the spherical surface by using a RANSAC algorithm.
Preferably, in step 215, a curvature threshold value current _ threshold =0.07 and a normal vector threshold value normal _ threshold = cosf (5.0/180.0 × m _pi)
Preferably, the specific process of calculating the spherical center and radius parameters of the spherical surface by using the RANSAC algorithm comprises the following steps:
step 2001, setting a spherical equation model, randomly extracting Nums sample points, and fitting the model;
step 2002, setting the tolerance range as sigma, finding out points within the tolerance range of the distance fitting curve, and counting the number of the points;
step 2003, setting the number of sampling iterations, randomly selecting Nums points again, and repeating the steps 2001) -2002) until the iteration is finished;
and step 2004, after each fitting, the tolerance range has corresponding data points, and the condition that the number of the data points is the maximum is found out, namely the final fitting result.
The invention also provides a terrain scanning and analyzing device based on the FARO scanner and the point cloud, which comprises:
the acquisition module is used for remotely controlling the FARO scanner to scan a complex scene to acquire scattered point cloud data;
the first calculation module is used for calculating a normal vector of the point cloud data by using a Principal Component Analysis (PCA) algorithm, segmenting the three-dimensional point cloud data by using an improved region growing method, and performing spherical fitting on a segmented point cloud cluster set through sampling consistency to calculate spherical parameters;
and the second calculation module is used for solving the conversion relation between the new coordinate system and the coordinate system of the scanner to acquire the pose of the probe vehicle in the scene according to the coordinates of the three spherical targets on the trolley and the coordinates of the new coordinate system established by the three spheres.
The invention utilizes the hardware support of the accurate point cloud data provided by the existing FARO scanner, improves the traditional region growing algorithm to segment the scattered point cloud data on the basis, improves the accurate segmentation speed of the large-range point cloud data, and provides stability for the realization of the spherical fitting algorithm. In addition, the point cloud data in the large memory is converted into the elevation map containing point height information in the small memory, so that a user can observe the terrain and obtain information conveniently. The invention has clear algorithm, complete system interface function, expansibility and flexibility.
Drawings
FIG. 1 is a flow chart of a terrain scanning analysis method based on a FARO scanner and a point cloud according to the present invention;
FIG. 2 is a schematic structural diagram of a terrain scanning and analyzing device based on a FARO scanner and a point cloud according to the present invention;
FIG. 3 is a point cloud model selected by the present invention;
FIG. 4 is a schematic diagram of a point cloud model after spherical segmentation and marking according to the present invention;
FIG. 5 is a diagrammatic illustration of conversion of point cloud data to TIF elevation in accordance with the present invention.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
As shown in fig. 1, the present invention provides a terrain scanning analysis method based on a FARO scanner and a point cloud, including:
step 2, calculating a normal vector of the point cloud data by utilizing a PCA (Principal component analysis) algorithm, segmenting the three-dimensional point cloud data by utilizing an improved region growing method, and performing spherical surface fitting on the segmented point cloud cluster set through sampling consistency to calculate spherical surface parameters;
and 3, solving the conversion relation between the new coordinate system and the coordinate system of the scanner to acquire the pose of the probe vehicle in the scene according to the coordinates of the three spherical targets on the trolley and the coordinates of the new coordinate system established by the three spheres.
Further, in the step 2, a PCA principal component analysis method is used for calculating a normal vector of the point cloud data, and calculation is carried out through a locally fitted surface of an area where the point cloud is located, and the specific process comprises the following steps:
step 201, initializing an input point cloud object cloudFind;
step 202, initializing a Kdtree object search _ tree;
step 203, loading the closed _ find into a Kdtree for traversing query;
step 204, initializing normal estimation object normalletiming;
step 205, setting normalcestimination to search in a search _ tree mode;
step 206, setting a search Radius =0.06;
step 207, initializing an output normal data set object, closed _ with _ normals;
step 208, for each point p in the point cloud cluster:
(1) Searching nearest neighbor elements of the p point to obtain searchForNeighbors;
(2) Calculating the surface normal n of the point p by using a computePoint normal method;
(3) The normal direction is calculated by PCA and due to ambiguity it has to be checked if the direction of n points consistently to the viewpoint and if not flipped using the setViewPoint () method.
Further, the point cloud segmentation specific process in the step 2 is as follows:
step 209, setting k =20 of k neighbor search;
step 210, initializing an index k _ neighbor _ index of k neighbor domain search;
step 211, initializing a distance k _ neighbor _ index _ dis between index points searched by the k neighbor domain;
step 212, initializing and saving the curvature of each point region position and an indexed collection object vec _ current;
step 213, traversing the input point cloud object circle _ find, calculating the curvature of each point p and the corresponding index position by taking k, k _ neighbor _ index and k _ neighbor _ index _ dis as parameters, and storing the curvature and the corresponding index position in the vec _ current set;
step 214, sorting the curvatures in the vec _ current set from small to large by using a sort function;
step 215, setting curvature threshold value current _ threshold =0.07, normal vector threshold value normal _ threshold = cosf (5.0/180.0 × m _pi);
step 216, setting the point with the minimum curvature as an initial seed point seed _ original, and ensuring that the algorithm starts from the most smooth area in the point cloud to reduce the segmentation number;
step 217, traversing the seed queue seed, testing the included angle between each adjacent point and the normal vector of the seed point, and if the included angle is smaller than a critical value, adding the included angle into the current seed area; testing the curvature of each adjacent point, and if the curvature is smaller than a critical value, adding the point into a seed list of the current area; until all seed queues are judged;
step 218, storing the segmented results meeting the conditions in a cluster;
step 219, traversing the cluster point set lists cluster, performing spherical surface fitting on each cluster by using a sampling consistency segmentation algorithm, and calculating the spherical center and radius parameters of the spherical surface by using a RANSAC algorithm.
Further, the specific process of calculating the spherical center and radius parameters of the spherical surface by using the RANSAC algorithm is as follows:
step 2001, setting a spherical equation model, randomly extracting Nums sample points, and fitting the model;
step 2002, setting the tolerance range as sigma, finding out points within the tolerance range of the distance fitting curve, and counting the number of the points;
step 2003, setting the number of sampling iterations, randomly selecting Nums points again, and repeating the steps 2001) -2002) until the iteration is finished;
and step 2004, after each fitting, the tolerance range has corresponding data points, and the condition that the number of the data points is the maximum is found out, namely the final fitting result.
As shown in fig. 2, the present invention provides a terrain scanning analysis device based on a FARO scanner and a point cloud, comprising:
the acquisition module is used for remotely controlling the FARO scanner to scan a complex scene to acquire scattered point cloud data;
the first calculation module is used for calculating a normal vector of the point cloud data by utilizing a Principal Component Analysis (PCA) algorithm, dividing the three-dimensional point cloud data by utilizing an improved region growing method, and performing spherical surface fitting on a divided point cloud cluster set through sampling consistency to calculate a spherical surface parameter;
and the second calculation module is used for solving the conversion relation between the new coordinate system and the coordinate system of the scanner per se according to the coordinates of the three spherical targets on the trolley and the coordinates of the new coordinate system established by the three spheres so as to acquire the pose of the probe vehicle in the scene.
The invention utilizes the characteristic that the spherical target has general regular symmetry, establishes a mathematical model by means of a space spherical equation and quickly fits a three-dimensional coordinate of the center of the target sphere by means of an improved recognition algorithm so as to solve the conversion of a point cloud coordinate system and finally realize the accurate positioning.
Example 1:
the invention provides a terrain scanning analysis method based on a FARO scanner and a point cloud, which comprises the following steps:
step 1), connecting a FARO scanner according to the IP address;
step 2), setting parameters of scanning resolution, measurement rate, noise compression, vertical angle and horizontal angle;
step 3), starting to scan and acquiring scattered point cloud data as shown in FIG. 3;
step 4), calculating a point cloud normal vector by using a PAC (programmable automation controller) principal component analysis method;
step 5), partitioning the point cloud according to an improved region growing algorithm;
step 6), performing spherical surface fitting on the segmented point cloud cluster set, and calculating the spherical center and radius parameters of a target ball, as shown in FIG. 4;
step 7), establishing a new coordinate system, converting the coordinate system, and outputting the matrix information to the disk text
Step 8), the point cloud data is loaded again, converted into a TIF elevation map only containing one height channel, and output to a local disk, as shown in FIG. 5;
and 9), opening a sub-window, loading point cloud data to preview and edit the terrain.
According to the terrain scanning analysis method based on the FARO scanner and the point cloud, the authority for controlling the FRAO S70 scanner is obtained through the local area network IP address, and the opening and closing of the scanner and the setting of key parameters are remotely controlled. Scanning a complex scene to obtain scattered three-dimensional point cloud data, firstly calculating a normal vector, then segmenting the point cloud through an improved region growing algorithm provided by the text, segmenting the point cloud into point cloud slice clusters, and screening out a point cloud cluster containing a spherical surface. And then, fitting the spherical center coordinates and the radius of the spherical target by utilizing a point cloud segmentation technology, namely a sampling consistency segmentation algorithm. And solving the transformation relation of the two coordinate systems to acquire the pose of the trolley according to the coordinates of the three spherical targets on the detection trolley in the scene and the coordinates of a new coordinate system established by the three spheres, and providing parameters for the precision evaluation of the binocular camera. And converting the point cloud data into a TIF format elevation map of a regular grid according to the point cloud data, wherein the height value, namely the Z value, of the position in the scene is stored in each pixel position, and an evaluator can check the parameters. The main window is used for displaying point cloud data scanned in real time and marking the position and the color of a characteristic object, and the sub-interface comprises two interfaces, namely a terrain preview interface and an editing interface. Meanwhile, the system has the function of communicating with other service terminals, and realizes the functions of end-to-end data transmission and instruction driving system work.
It should be noted that although in the above detailed description several components or units of the device are mentioned for each functional implementation, such a division is not mandatory. Indeed, the features and functionality of two or more components or units described above may be embodied in one component or unit, according to embodiments of the invention. Conversely, the features and functions of one component or unit described above may be further divided into embodiments by a plurality of components or units.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, the claims should be construed to include preferred embodiments and all changes and modifications that fall within the scope of the invention.
Claims (5)
1. A terrain scanning analysis method based on a FARO scanner and a point cloud is characterized by comprising the following steps:
step 1, remotely controlling a FARO scanner to scan a complex scene to obtain scattered point cloud data;
step 2, calculating a normal vector of the point cloud data by using a PCA (principal component analysis) algorithm, segmenting the three-dimensional point cloud data by using an improved region growing method, and performing spherical fitting on a segmented point cloud cluster set through sampling consistency to calculate spherical parameters;
step 3, solving a conversion relation between a new coordinate system and a coordinate system of the FARO scanner to acquire the pose of the detection vehicle in the scene according to the coordinates of three spherical targets on the detection vehicle in the complex scene and the coordinates of the new coordinate system established by the three spheres;
in the step 2, a PCA principal component analysis method is used for calculating a point cloud data normal vector, and calculation is carried out through the surface of local fitting of the area where the point cloud is located, and the specific process comprises the following steps:
step 201, initializing an input point cloud object cloudFind;
step 202, initializing a Kdtree object search _ tree;
step 203, loading the closed _ find into a Kdtree for traversing query;
step 204, initializing normal estimation object normalletiming;
step 205, setting normallestimation to search in a search _ tree mode;
step 206, setting a search Radius;
step 207, initializing an output normal data set object, closed _ with _ normals;
step 208, for each point p in the point cloud cluster:
(1) Searching nearest neighbor elements of the p point to obtain searchForNeighbors;
(2) Calculating the surface normal n of the point p by using a computePoint normal method;
(3) Calculating a normal direction through PCA, checking whether the direction of n points to a viewpoint consistently, and if not, turning over by using a setViewPoint () method;
the point cloud segmentation in the step 2 comprises the following specific processes:
step 209, setting k =20 of k neighbor search;
step 210, initializing an index k _ nebor _ index of k neighbor domain search;
step 211, initializing a distance k _ neighbor _ index _ dis between index points searched by the k neighbor domain;
step 212, initializing a set object vec _ current for storing the curvature and the index of each point region position;
step 213, traversing the input point cloud object circle _ find, calculating the curvature of each point p and the corresponding index position by taking k, k _ neighbor _ index and k _ neighbor _ index _ dis as parameters, and storing the curvature and the corresponding index position in the vec _ current set;
step 214, sorting the curvatures in the vec _ current set from small to large by using a sort function;
step 215, setting a curvature threshold and a normal vector threshold;
step 216, setting the point with the minimum curvature as an initial seed point seed _ original;
step 217, traversing the seed queue seed, testing the included angle between each adjacent point and the normal vector of the seed point, and if the included angle is smaller than a critical value, adding the included angle into the current seed area; testing the curvature of each adjacent point, and if the curvature is smaller than a critical value, adding the point into a seed list of the current area; until all seed queues are judged;
step 218, storing the segmented results meeting the above conditions in a clusters;
step 219, traversing the cluster point set lists cluster, performing spherical surface fitting on each cluster by using a sampling consistency segmentation algorithm, and calculating the spherical center and radius parameters of the spherical surface by using a RANSAC algorithm.
2. The method for analyzing a terrain scan based on a FARO scanner and point cloud as claimed in claim 1, wherein in step 206, the search Radius =0.06.
3. A method for terrain scan analysis based on FARO scanners and point clouds as in claim 1, where in step 215 a curvature threshold value current _ threshold =0.07, and a normal vector threshold value normal _ threshold = cosf (5.0/180.0 m _pi).
4. The method for terrain scanning analysis based on a FARO scanner and point cloud as claimed in claim 1, wherein the RANSAC algorithm is used to calculate the spherical center and radius parameters of the spherical surface by the following specific process:
step 2001, setting a spherical equation model, randomly extracting Nums sample points, and fitting the model;
step 2002, setting the tolerance range as sigma, finding out points within the tolerance range of the distance fitting curve, and counting the number of the points;
step 2003, setting the number of sampling iterations, randomly selecting Nums points again, and repeating the steps 2001) -2002) until the iteration is finished;
and step 2004, after each fitting, the tolerance range has corresponding data points, and the condition that the number of the data points is the maximum is found out, namely the final fitting result.
5. A terrain scan analysis apparatus based on a FARO scanner and a point cloud, which is realized by the method of any one of claims 1 to 4, comprising:
the acquisition module is used for remotely controlling the FARO scanner to scan a complex scene to acquire scattered point cloud data;
the first calculation module is used for calculating a normal vector of the point cloud data by using a Principal Component Analysis (PCA) algorithm, segmenting the three-dimensional point cloud data by using an improved region growing method, and performing spherical fitting on a segmented point cloud cluster set through sampling consistency to calculate spherical parameters;
and the second calculation module is used for solving the conversion relation between the new coordinate system and the coordinate system of the FARO scanner to acquire the pose of the detection vehicle in the scene according to the coordinates of the three spherical targets on the detection vehicle in the complex scene and the coordinates of the new coordinate system established by the three spheres.
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