CN107886528B - Distribution line operation scene three-dimensional reconstruction method based on point cloud - Google Patents

Distribution line operation scene three-dimensional reconstruction method based on point cloud Download PDF

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CN107886528B
CN107886528B CN201711242672.3A CN201711242672A CN107886528B CN 107886528 B CN107886528 B CN 107886528B CN 201711242672 A CN201711242672 A CN 201711242672A CN 107886528 B CN107886528 B CN 107886528B
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CN107886528A (en
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郭毓
陈宝存
吴巍
吴禹均
饶志强
郭健
吴益飞
郭飞
肖潇
蔡梁
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Nanjing University of Science and Technology
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Abstract

The invention discloses a distribution line operation scene three-dimensional reconstruction method based on point cloud, which comprises the steps of obtaining part point cloud of a distribution line to be reconstructed, such as a lightning arrester, a cross arm and the like, by performing operations of conditional filtering, downsampling, outlier removing, segmentation and the like on initial point cloud, then extracting key points by adopting an SIFT 3D algorithm, constructing key point description vectors by using FPFH (field programmable gate array) characteristics, completing point cloud registration by adopting rough registration and improved ICP (inductively coupled plasma) precise registration to obtain complete three-dimensional point cloud of parts, namely establishing an offline model base, then collecting the point cloud in real time, sequentially registering the part point cloud with the model base model to complete three-dimensional reconstruction, and finally completing curved surface reconstruction by using a Poisson curved surface reconstruction method to obtain a three-dimensional model. According to the invention, semi-autonomous three-dimensional reconstruction of a distribution line scene is realized, manual intervention is reduced, SIFT 3D key point extraction and FPFH (field programmable gate flash) feature description vectors are adopted aiming at the important step of three-dimensional reconstruction, the quality of key points is ensured, weight is set for point pair relation, wrong point pairs are eliminated, the registration speed is accelerated, and the efficiency of three-dimensional reconstruction is improved.

Description

Distribution line operation scene three-dimensional reconstruction method based on point cloud
Technical Field
The invention relates to the field of live working robot environment perception, in particular to a distribution line working scene three-dimensional reconstruction method based on point cloud.
Background
With the development of robot technology, robots play an increasingly important role in various fields. The robot technology is applied to the power industry, the electric power maintenance and overhaul work is carried out instead of manpower, and the safety and the efficiency of operation can be improved to a great extent.
The robot is adopted to carry out live working, a teleoperation mode and an autonomous mode are generally adopted, and no matter which mode is adopted, three-dimensional reconstruction needs to be carried out on a working scene, so that firstly, visual presence is provided, and teleoperation personnel can carry out man-machine interaction operation with strong immersion based on virtual reality; secondly, the robot has scene perception capability and can carry out autonomous obstacle avoidance and motion planning
The point cloud-based three-dimensional reconstruction mainly comprises point cloud preprocessing, point cloud registration and curved surface reconstruction. The current commonly used registration method is to combine coarse registration and fine registration. For coarse registration, usually, geometric features of point clouds are extracted first to prepare for establishing a corresponding relationship of the point clouds, and the currently popular coarse registration method based on RANSAC has strong randomness, influences registration efficiency and reduces modeling speed. For the precise registration, an ICP algorithm is generally adopted, but the conventional ICP algorithm takes all point sets as point sets to be registered, and does not exclude wrong point pairs, which not only affects the speed of registration, but also reduces the precision of registration
Disclosure of Invention
The invention aims to provide a three-dimensional reconstruction method of a distribution line operation scene based on point cloud.
The technical scheme for realizing the purpose of the invention is as follows: a distribution line operation scene three-dimensional reconstruction method based on point cloud comprises the following steps:
step 1, collecting a working scene point cloud and carrying out preprocessing operation on the working scene point cloud;
step 2, segmenting the point cloud scene by adopting a color region growing method;
step 3, registering the point clouds under multiple visual angles by adopting a coarse registration method and a fine registration method;
step 4, establishing an offline model library comprising the lightning arrester and the cross arm;
step 5, performing real-time three-dimensional reconstruction on the operation scene;
and 6, performing curved surface reconstruction on the point cloud obtained in the step 5 by adopting a Poisson algorithm.
Compared with the prior art, the invention has the following remarkable advantages: aiming at the particularity of a distribution line scene, the invention adopts an effective preprocessing method to obtain the point cloud of the parts, optimizes the registration method and improves the speed of three-dimensional reconstruction. And for the distribution line operation scene, three-dimensional reconstruction can be performed semi-autonomously, so that manual intervention is reduced, and the efficiency of operation scene reconstruction is improved.
The present invention is described in further detail below with reference to the attached drawing figures.
Drawings
Fig. 1 is a three-dimensional reconstruction flow chart of a distribution line operation scene based on point cloud.
Fig. 2 is a flow chart of an improved point cloud registration.
Fig. 3 is a schematic view of multi-view point cloud registration.
Fig. 4 is a flow chart of replacing the real-time local point cloud with a corresponding model.
Fig. 5 is a diagram of a distribution line scene preprocessing result.
Fig. 6 is a schematic diagram of distribution line scene point cloud segmentation.
Fig. 7 is a distribution line scene point cloud segmentation result diagram, wherein diagram (a) is a cross-arm point cloud, diagram (b) is a left arrester point cloud, and diagram (c) is a right arrester point cloud.
Fig. 8 is a schematic diagram of cross-arm registration at two viewing angles, where (a) is two cross-arm point clouds before registration and (b) is two cross-arm point clouds after registration.
Fig. 9 is a schematic view of registration of arresters at two viewing angles, where diagram (a) is two arrester point clouds before registration and diagram (b) is two arrester point clouds after registration.
Detailed Description
The following describes a specific embodiment of the distribution line operation scene three-dimensional reconstruction method based on point cloud with reference to the accompanying drawings:
for the stage of establishing a model base in an off-line mode, a visual collection device is held by hands to move around objects in a scene to collect point cloud data under multiple visual angles; for real-time display of the operation scene, the visual device is fixed outside the operation scene by a certain distance.
The distribution line operation scene three-dimensional reconstruction flow chart based on point cloud is shown in fig. 1, and comprises the following steps:
step 1, collecting operation scene point clouds and carrying out preprocessing operation on the operation scene point clouds; the method comprises the following specific steps:
step 1-1, selecting an interested area by adopting a conditional filtering method:
because the point cloud is a set of three-dimensional coordinates, the range of the point cloud in the x, y and z directions can be limited according to the prior knowledge, and the region where the scene is probably located is determined;
step 1-2, performing self-adaptive voxel down-sampling on the point cloud obtained in the step 1-1, and removing outliers; establishing a three-dimensional voxel grid for the point cloud, assuming that the edge length of a cube is linearly related to the average nearest neighbor Euclidean distance of the point cloud, and approximately representing the voxel by using the gravity center of a point set in each voxel to realize the down-sampling of the point cloud; removing sparse outliers by adopting a statistical-based method;
step 2, automatically dividing the red lightning arrester and the gray cross arm by adopting a color area increasing method;
step 3, registering the point clouds of the same object under two visual angles by adopting a coarse registration method and a fine registration method, as shown in fig. 2, and specifically comprising the following steps:
step 3-1, extracting key points of the point cloud by adopting an SIFT 3D algorithm, and calculating the FPFH (fuzzy programming frequency) characteristics of the key points, specifically:
step 3-1-1, detecting the characteristic points of the scale space, wherein the used scale space and Gaussian difference function are as follows:
scale space: l (x, y, z, σ) G (x, y, z, σ) P (x, y, z)
Gaussian difference function: d (x, y, z, k)1 iσ)＝L(x,y,z,k1 (i+1)σ)-L(x,y,z,k1 iσ),i∈[0,s+2]
Wherein G (x, y, z, σ) is a Gaussian nucleus,
Figure BDA0001490151460000031
p (x, y, z) is a point in the point cloud, σ is a scale space factor, k1Is a constant multiplication factor, and s is the number of layers in the pyramid group;
step 3-1-2, removing feature points and edge response points with low contrast, specifically:
substituting the characteristic points (x, y, z) into the Gaussian difference function, and if the absolute value of the obtained value is greater than the threshold tau1If not, the data is retained, otherwise, the data is removed; then removing the edge points;
3-1-3, determining the main direction of the key point; the amplitude m (x, y, z), the azimuth angle theta (x, y, z) and the pitch angle phi (x, y, z) from the key point and the k neighborhood point to the neighborhood center point are respectively as follows:
Figure BDA0001490151460000032
θ(x,y,z)＝tan-1((yi-yc)/(xi-xc))
φ(x,y,z)＝sin-1((zi-zc)/m(x,y,z))
wherein (x)i,yi,zi) (i ═ 1, 2., k, k +1) as the keypoint and its k neighborhood, (x) as the keypointc,yc,zc) Is the center point of the neighborhood; counting an azimuth angle theta (x, y, z) and a pitch angle phi (x, y, z) in a key point neighborhood by using a histogram, taking an amplitude value m (x, y, z) as a weight, and selecting a main peak value of the histogram as a main direction of a key point;
step 3-1-4, establishing FPFH characteristic description of key points:
Figure BDA0001490151460000033
wherein, PiIs a key point, PkIs close toNeighbors by PiAnd PkIs taken as the weight omegakSPFH is a simple point feature histogram;
step 3-2, carrying out rough registration based on sampling consistency on point clouds under different visual angles, wherein the specific process is as follows:
step 3-2-1, randomly selecting s key points from the source point cloud P, and ensuring that the distance between each point and each point is larger than a preset minimum distance dmin；
Step 3-2-2, for each key point siFinding and s in the target point cloud QiA set of points with similar FPFH characteristics, from which a point is randomly drawn to represent a sample point siThe corresponding point of (a);
step 3-2-3, estimating a rigid body transformation matrix by a set containing s point pairs, and evaluating the quality of rigid body transformation by calculating an error metric, wherein the error metric is usually calculated by a Huber evaluation formula:
Figure BDA0001490151460000041
wherein e isiRepresenting Euclidean distance, t, of the ith point pair after rigid body transformationeIs a constant number, Lh(ei) Error metric for the ith point pair;
3-2-4, if the error reaches the expected range or reaches the maximum iteration time m, ending the iteration process, otherwise, returning to the step 3-2-1;
3-3, performing fine registration based on an improved ICP algorithm on the point clouds under different viewing angles, specifically:
step 3-3-1, determining corresponding point pairs: searching a key point set { P) in a source point cloud P in a target point cloud QiCorresponding set of closest points qi}；
Step 3-3-2, determining the point pair weight:
Figure BDA0001490151460000042
wherein DistmaxIs the maximum value of the distances between all the point pairs, weightiGiving a threshold t for the weight of each point pair, if weightiIf t is less than t, the point is removed;
3-3-3, estimating a rotation matrix R and a translation matrix T by adopting an SVD (singular value decomposition) method, performing rotation transformation and translation transformation on the source point cloud P, and calculating an error and a function:
Figure BDA0001490151460000043
3-3-4, judging whether the error sum is smaller than a threshold tau, judging whether the maximum iteration number n is reached, finishing the fine registration if the maximum iteration number n is met, and returning to the 3-3-1 if the error sum is not smaller than the threshold tau;
step 4, establishing an off-line model library comprising a lightning arrester Q1And cross arm Q2(ii) a The method specifically comprises the following steps:
step 4-1, acquiring point clouds of an operation scene under multiple visual angles, and respectively obtaining arrester point clouds and cross arm point clouds under the multiple visual angles through the preprocessing of the step 1 and the point cloud segmentation of the step 2;
and 4-2, registering the point clouds under other visual angles to the visual angle through the step 3 by taking the visual angle 1 as a reference point cloud, and forming complete arrester point cloud and cross arm point cloud, namely completing the construction of an offline model library. As shown in fig. 3;
step 5, finishing the real-time three-dimensional reconstruction of the operation scene; as shown in fig. 4, the specific steps are as follows:
step 5-1, collecting scene point cloud data in real time;
step 5-2, carrying out the pretreatment of the step 1, and carrying out automatic segmentation by adopting the method of the step 2;
step 5-3, dividing the result PiRespectively adopting the method of the step 3 and the model library lightning arrester point cloud Q1And cross-arm point cloud Q2Carrying out registration to obtain registration error eij(j＝1,2)；
Step 5-4, taking the model with small registration error result as PiAnd performing rigid body transformation on the model point cloud to replace the current point cloud Pi
And 6, performing curved surface reconstruction on the point cloud obtained in the step 5 by adopting a Poisson curved surface reconstruction algorithm.
According to the invention, semi-autonomous three-dimensional reconstruction of a distribution line scene is realized, manual intervention is reduced, SIFT 3D key point extraction and FPFH (field programmable gate flash) feature description vectors are adopted aiming at the important step of three-dimensional reconstruction, the quality of key points is ensured, weight is set for point pair relation, wrong point pairs are eliminated, the registration speed is accelerated, and the efficiency of three-dimensional reconstruction is improved.
The present invention will be described in further detail with reference to examples.
Examples
(1) Object
And aiming at a simulated real distribution line scene built according to the power standard, scene point cloud is collected through kinect 2.
(2) Results of the process
The method of the invention is characterized by three processes: preprocessing, segmenting and registering, and the experimental simulation effect of the method is shown in the following three aspects.
The operation can select an interested area in an operation scene, uniformly sample originally dense point clouds to ensure that the number of the point clouds is proper, and remove the interference caused by noise.
Then, the automatic segmentation of the lightning arrester and the cross arm is realized by adopting a color region growing method, a segmentation effect graph is shown in fig. 6, and a segmentation result graph is shown in fig. 7.
The point clouds of the same object under different visual angles are registered to the same visual angle, and schematic diagrams before and after the registration of the cross arm and the lightning arrester are respectively shown in fig. 8 and 9 by using the method.
Compared with a method based on RANSAC coarse registration and combined with traditional ICP registration, the method for comparing the accuracy and the speed of lightning arrester registration is shown in Table 1
TABLE 1
Figure BDA0001490151460000061
As shown in Table 1, the method of the invention has shorter time and higher registration precision.
(3) Results
Based on the preprocessing operation, the point cloud segmentation and the point cloud registration technology, a complete off-line model of each part is established, real-time data is registered with the off-line model, and finally three-dimensional reconstruction of a distribution line operation scene is completed.

Claims (3)

1. A distribution line operation scene three-dimensional reconstruction method based on point cloud is characterized by comprising the following steps:
step 1, collecting operation scene point clouds and carrying out pretreatment operation on the operation scene point clouds, specifically carrying out condition filtering, down-sampling and outlier removal treatment;
step 2, segmenting the point cloud scene by adopting a color region growing method;
step 3, registering the point clouds under multiple visual angles by adopting a coarse registration method and a fine registration method, which specifically comprises the following steps:
step 3-1, extracting key points of the point cloud by adopting an SIFT 3D algorithm, and calculating the FPFH (fuzzy programming frequency) characteristics of the key points, specifically:
step 3-1-1, detecting the characteristic points of the scale space, wherein the used scale space and Gaussian difference function are as follows:
scale space: l (x, y, z, σ) G (x, y, z, σ) P (x, y, z)
Gaussian difference function: d (x, y, z, k)1 iσ)＝L(x,y,z,k1 (i+1)σ)-L(x,y,z,k1 iσ),i∈[0,s+2]
Wherein G (x, y, z, σ) is a Gaussian nucleus,
Figure FDA0003027467860000011
p (x, y, z) is a point in the point cloud, σ is a scale space factor, k1Is a constant multiplication factor, and s is the number of layers in the pyramid group;
step 3-1-2, removing feature points and edge response points with low contrast, specifically:
substituting the characteristic points (x, y, z) into the Gaussian difference function, if the absolute value of the obtained value is larger than the threshold valueτ1If not, the data is retained, otherwise, the data is removed; then removing the edge points;
3-1-3, determining the main direction of the key point; the amplitude m (x, y, z), the azimuth angle theta (x, y, z) and the pitch angle phi (x, y, z) from the key point and the k neighborhood point to the neighborhood center point are respectively as follows:
Figure FDA0003027467860000012
θ(x,y,z)＝tan-1((yi-yc)/(xi-xc))
φ(x,y,z)＝sin-1((zi-zc)/m(x,y,z))
wherein (x)i,yi,zi) (i ═ 1, 2., k, k +1) as the keypoint and its k neighborhood, (x) as the keypointc,yc,zc) Is the center point of the neighborhood; counting an azimuth angle theta (x, y, z) and a pitch angle phi (x, y, z) in a key point neighborhood by using a histogram, taking an amplitude value m (x, y, z) as a weight, and selecting a main peak value of the histogram as a main direction of a key point;
step 3-1-4, establishing FPFH characteristic description of key points:
Figure FDA0003027467860000013
wherein, PiIs a key point, PkBeing neighbors, with PiAnd PkIs taken as the weight omegakSPFH is a simple point feature histogram;
step 3-2, carrying out rough registration based on sampling consistency on point clouds under different visual angles, wherein the specific process is as follows:
step 3-2-1, randomly selecting s key points from the source point cloud P, and ensuring that the distance between each point and each point is larger than a preset minimum distance dmin；
Step 3-2-2, for each key point siFinding and s in the target point cloud QiSet of points with similar FPFH characteristics, from which a point is randomly extractedTo represent a sample point siThe corresponding point of (a);
step 3-2-3, estimating a rigid body transformation matrix by a set containing s point pairs, and evaluating the quality of rigid body transformation by calculating an error metric, wherein the error metric is usually calculated by a Huber evaluation formula:
Figure FDA0003027467860000021
wherein e isiRepresenting Euclidean distance, t, of the ith point pair after rigid body transformationeIs a constant number, Lh(ei) Error metric for the ith point pair;
3-2-4, if the error reaches the expected range or reaches the maximum iteration time m, ending the iteration process, otherwise, returning to the step 3-2-1;
3-3, performing fine registration based on an improved ICP algorithm on the point clouds under different viewing angles, specifically:
step 3-3-1, determining corresponding point pairs: searching a key point set { P) in a source point cloud P in a target point cloud QiCorresponding set of closest points qi}；
Step 3-3-2, determining the point pair weight:
Figure FDA0003027467860000022
wherein DistmaxIs the maximum value of the distances between all the point pairs, weightiGiving a threshold t for the weight of each point pair, if weightiIf t is less than t, the point is removed;
3-3-3, estimating a rotation matrix R and a translation matrix T by adopting an SVD (singular value decomposition) method, performing rotation transformation and translation transformation on the source point cloud P, and calculating an error and a function:
Figure FDA0003027467860000023
3-3-4, judging whether the error sum is smaller than a threshold tau, judging whether the maximum iteration number n is reached, finishing the fine registration if the maximum iteration number n is met, and returning to the 3-3-1 if the error sum is not smaller than the threshold tau;
step 4, establishing an offline model library, which comprises the lightning arrester and the cross arm;
step 5, performing real-time three-dimensional reconstruction on the operation scene;
and 6, performing curved surface reconstruction on the point cloud obtained in the step 5 by adopting a Poisson algorithm.
2. The point cloud-based three-dimensional reconstruction method for distribution line operation scene according to claim 1, wherein step 4 is to establish an offline model library including a lightning arrester Q1And cross arm Q2The method specifically comprises the following steps:
step 4-1, acquiring point clouds of an operation scene under multiple visual angles, and respectively obtaining arrester point clouds and cross arm point clouds under the multiple visual angles through the preprocessing of the step 1 and the point cloud segmentation of the step 2;
and 4-2, registering the point clouds under other visual angles to the visual angle through the step 3 by taking the visual angle 1 as a reference point cloud, and forming complete arrester point cloud and cross arm point cloud, namely completing the construction of an offline model library.
3. The point cloud-based three-dimensional reconstruction method for the distribution line work scene according to claim 1, wherein the step 5 is used for performing real-time three-dimensional reconstruction on the work scene, and specifically comprises the following steps:
step 5-1, collecting scene point cloud data in real time;
step 5-2, preprocessing the acquired data in the step 1, and then automatically segmenting by adopting the method in the step 2;
step 5-3, dividing the result PiRespectively adopting the method of the step 3 and the model library lightning arrester point cloud Q1And cross-arm point cloud Q2Carrying out registration to obtain registration error eij(j＝1,2)；
Step 5-4, taking the model with small registration error result as PiAnd performing rigid body transformation on the model point cloud to replace the current point cloud Pi。
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