Improved algorithm for point cloud registration based on fast point feature histograms

Wang, Jian; Zhao, Yonghui; Wang, Yanxia; Yao, Yi

doi:10.1117/1.jrs.10.045024

Cited by 18 publications

(11 citation statements)

References 28 publications

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“…The number of points contained in the i voxel is K i and the points in the i-th voxel are expressed as P i = {p 1 , p 2 ..., p K i }. If the number of points in the i voxel is K i > 0, then the R i , G i , and B i values of the i-th small block are calculated by Equation (7).…”

Section: Voxelizationmentioning

confidence: 99%

“…For the feature extraction of point clouds, researchers proposed a large number of feature descriptors including, for example, normal vector, elevation feature [1], spin image [2], covariance eigenvalue feature [3,4], global feature viewpoint feature histogram (view feature histogram, VFH) [5], clustered view feature histogram (CVFH) [6], and fast point feature histogram [7] (fast point feature histograms, FPFH). However, the abovementioned features are all extracted from the geometric structure information of the point cloud and lack the use of the color information of the color point cloud.…”

Section: Introductionmentioning

confidence: 99%

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Point Cloud Classification Algorithm Based on the Fusion of the Local Binary Pattern Features and Structural Features of Voxels

Luo

et al. 2021

Remote Sensing

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Point cloud classification is a key technology for point cloud applications and point cloud feature extraction is a key step towards achieving point cloud classification. Although there are many point cloud feature extraction and classification methods, and the acquisition of colored point cloud data has become easier in recent years, most point cloud processing algorithms do not consider the color information associated with the point cloud or do not make full use of the color information. Therefore, we propose a voxel-based local feature descriptor according to the voxel-based local binary pattern (VLBP) and fuses point cloud RGB information and geometric structure features using a random forest classifier to build a color point cloud classification algorithm. The proposed algorithm voxelizes the point cloud; divides the neighborhood of the center point into cubes (i.e., multiple adjacent sub-voxels); compares the gray information of the voxel center and adjacent sub-voxels; performs voxel global thresholding to convert it into a binary code; and uses a local difference sign–magnitude transform (LDSMT) to decompose the local difference of an entire voxel into two complementary components of sign and magnitude. Then, the VLBP feature of each point is extracted. To obtain more structural information about the point cloud, the proposed method extracts the normal vector of each point and the corresponding fast point feature histogram (FPFH) based on the normal vector. Finally, the geometric mechanism features (normal vector and FPFH) and color features (RGB and VLBP features) of the point cloud are fused, and a random forest classifier is used to classify the color laser point cloud. The experimental results show that the proposed algorithm can achieve effective point cloud classification for point cloud data from different indoor and outdoor scenes, and the proposed VLBP features can improve the accuracy of point cloud classification.

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Section: Voxelizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Point Cloud Classification Algorithm Based on the Fusion of the Local Binary Pattern Features and Structural Features of Voxels

Luo

et al. 2021

Remote Sensing

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“…It should provide a concise and precise description of the experimental results, their interpretation as well as the experimental conclusions that can be drawn. The FIPP algorithm [28] is mainly used in point cloud registration. It searches the correspondences of the point pairs for registration from two point cloud datasets within three constraints: features of points, distances between points, and location relationships.…”

Section: Searching Strategy With Fipp Algorithmmentioning

confidence: 99%

“…The FIPP algorithm has been used in point cloud registration without RGB value [28], which used a point descriptor as the feature constraint. The results show that the accuracy of the FIPP algorithm with a descriptor is good in five types of point cloud datasets.…”

Section: Candidate Point Set Based On Rgb Valuementioning

confidence: 99%

Fast Method of Registration for 3D RGB Point Cloud with Improved Four Initial Point Pairs Algorithm

Wang

Gao

2019

Sensors

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Three-dimensional (3D) point cloud registration is an important step in three-dimensional (3D) model reconstruction or 3D mapping. Currently, there are many methods for point cloud registration, but these methods are not able to simultaneously solve the problem of both efficiency and precision. We propose a fast method of global registration, which is based on RGB (Red, Green, Blue) value by using the four initial point pairs (FIPP) algorithm. First, the number of different RGB values of points in a dataset are counted and the colors in the target dataset having too few points are discarded by using a color filter. A candidate point set in the source dataset are then generated by comparing the similarity of colors between two datasets with color tolerance, and four point pairs are searched from the two datasets by using an improved FIPP algorithm. Finally, a rigid transformation matrix of global registration is calculated with total least square (TLS) and local registration with the iterative closest point (ICP) algorithm. The proposed method (RGB-FIPP) has been validated with two types of data, and the results show that it can effectively improve the speed of 3D point cloud registration while maintaining high accuracy. The method is suitable for points with RGB values.

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“…On the other hand, a search strategy is used to find the point-to-point correspondence between the two datasets. The existing methods are RANdom SAmpling Consistency (RANSAC) [34], robust global registration [35], Greedy Initial Alignment (GIA) [30], 4-Point Coplanar (4PCS) [36], Four Initial Point Pairs (FIPP) [37], Evolutionary Calculation [38], etc. Global registration is currently the best way to solve the problem of ICP falling into a local optimum.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the ICP Algorithm in 3D Point Cloud Registration

Wang

et al. 2020

IEEE Access

Self Cite

121

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The iterative closest point (ICP) algorithm is widely used in three-dimensional (3D) point cloud registration, and it is very stable and robust. However, its biggest drawback is being easily trapped in a local optimal solution, which results in the incorrect registration result. Currently, there is neither a clear effective range to define whether the ICP algorithm will fall into a local optimum nor a study providing a comprehensive evaluation of the ICP algorithm. In this paper, we take the overlap ratio, angle, distance, and noise as the influencing factors of ICP and evaluate the validity, robustness, accuracy, and efficiency of point-to-point and point-to-plane ICP by using four datasets. We first analyze the effective ranges of the two ICP algorithms with respect to overlap ratio, angle, and distance and then propose a universal effective range for the three factors. Next, the effect of Gaussian noise on the validity and accuracy of two ICPs is evaluated. We also analyze the factors influencing ICP accuracy and explain their changing rules. We finally study the effect of different parameters on the efficiency. All results are compared by using point-to-point and point-to-plane ICP algorithms. The results show that the overlap ratio has no effect on validity, but it has a significant influence on accuracy. The angle has a great impact on the validity and efficiency of ICP but has no effect on accuracy. The distance only affects validity, has a limited effect on efficiency and no effect on accuracy. Meanwhile, Gaussian noise has a little effect on the validity. In addition, the general effective range of point-to-point ICP is larger than that of point-to-plane ICP, but the point-to-plane ICP algorithm presents a better efficiency. The point-to-point ICP is more robust to Gaussian noise with respect to validity, while the point-to-plane is more resistant in terms of accuracy.

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Improved algorithm for point cloud registration based on fast point feature histograms

Cited by 18 publications

References 28 publications

Point Cloud Classification Algorithm Based on the Fusion of the Local Binary Pattern Features and Structural Features of Voxels

Point Cloud Classification Algorithm Based on the Fusion of the Local Binary Pattern Features and Structural Features of Voxels

Fast Method of Registration for 3D RGB Point Cloud with Improved Four Initial Point Pairs Algorithm

Evaluation of the ICP Algorithm in 3D Point Cloud Registration

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