A Multi-Scale Covariance Matrix Descriptor and an Accurate Transformation Estimation for Robust Point Cloud Registration

Abstract

This paper presents a robust point cloud registration method based on a multi-scale covariance matrix descriptor and an accurate transformation estimation. Compared with state-of-the-art feature descriptors, such as FPH, 3DSC, spin image, etc., our proposed multi-scale covariance matrix descriptor is superior for dealing with registration problems in a higher noise environment since the mean operation in generating the covariance matrix can filter out most of the noise-damaged samples or outliers and also make itself robust to noise. Compared with transformation estimation, such as feature matching, clustering, ICP, RANSAC, etc., our transformation estimation is able to find a better optimal transformation between a pair of point clouds since our transformation estimation is a multi-level point cloud transformation estimator including feature matching, coarse transformation estimation based on clustering, and a fine transformation estimation based on ICP. Experiment findings reveal that our proposed feature descriptor and transformation estimation outperforms state-of-the-art feature descriptors and transformation estimation, and registration effectiveness based on our registration framework of point cloud is extremely successful in the Stanford 3D Scanning Repository, the SpaceTime dataset, and the Kinect dataset, where the Stanford 3D Scanning Repository is known for its comprehensive collection of high-quality 3D scans, and the SpaceTime dataset and the Kinect dataset are captured by a SpaceTime Stereo scanner and a low-cost Microsoft Kinect scanner, respectively.

Keywords

• point cloud registration
• feature descriptor
• multi-scale covariance matrix
• boundary point detection
• transformation estimation