Abstract
In this paper, we present an efficient algorithm for point cloud registration in presence of low overlap rate and high noise. The proposed registration method mainly includes four parts: the loop voxel filtering, the curvature-based key point selection, the robust geometric descriptor, and the determining and optimization of correspondences based on key point spatial relationship. The loop voxel filtering filters point clouds to a specified resolution. We propose a key point selection algorithm which has a better anti-noise and fast ability. The feature descriptor of key points is highly exclusive which is based on the geometric relationship between the neighborhood points and the center of gravity of the neighborhood. The correspondences in the pair of two point clouds are determined according to the combined features of key points. Finally, the singular value decomposition and ICP algorithm are applied to align two point clouds. The proposed registration method can accurately and quickly register point clouds of different resolutions in noisy situations. We validate our proposal by presenting a quantitative experimental comparison with state-of-the-art methods. Experimental results show that the proposed point cloud registration algorithm has faster calculation speed, higher registration accuracy, and better anti-noise performance.
Highlights
With the development of novel sensing technologies, such as Kinect, 3D LiDAR [1, 2] and terrestrial laser scanners (TLS), 3D point cloud becomes more convenient to acquire
Hong et al proposed a probabilistic normal distributions transform (PNDT) representation which improves the accuracy of point cloud registration by using the probabilities of point samples [29]
We propose a feature descriptor in which the local surface histogram is calculated according to the distance between the neighbor points and the gravity center of neighborhood of the key point, as well as normal of points in the neighborhood
Summary
With the development of novel sensing technologies, such as Kinect, 3D LiDAR [1, 2] and terrestrial laser scanners (TLS), 3D point cloud becomes more convenient to acquire. Mellado et al improved 4PCS and proposed SUPER 4PCS and speedups the registration process [24] Another idea of coarse registration is Sample Consensus algorithm. In the literature [26], during coarse registration stage, Random Sample Consensus (RANSAC) algorithm is used to obtain the transformation between two 3D point clouds. Hong et al proposed a probabilistic normal distributions transform (PNDT) representation which improves the accuracy of point cloud registration by using the probabilities of point samples [29]. Different from the above methods, this paper presents a key point selection algorithm which has a better anti-noise and fast ability. Experimental results show that the proposed point cloud registration algorithm has faster calculation speed, higher registration accuracy, and better anti-noise performance.
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