Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge |
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| Institution: | 1. School of Computer Science, The University of Adelaide, Australia;2. Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland;1. Department of Mathematics, Shanghai University, Shanghai 200444, China;2. Institute of NanoMicroEnergy, Shanghai University, Shanghai 200444, China;1. Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, Xiamen, China;2. Department of Geography and Environmental Management, University of Waterloo, Waterloo, Canada;3. College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China;4. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;1. Department of Geomatics, National Cheng Kung University, Taiwan;2. Department of Electrical and Computer Engineering, Old Dominion University, USA;1. Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstr. 7, 76131 Karlsruhe, Germany;2. Université Paris-Est, IGN, SRIG, MATIS, 73 Avenue de Paris, 94160 Saint-Mandé, France |
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| Abstract: | Automatic 3D point cloud registration is a main issue in computer vision and remote sensing. One of the most commonly adopted solution is the well-known Iterative Closest Point (ICP) algorithm. This standard approach performs a fine registration of two overlapping point clouds by iteratively estimating the transformation parameters, assuming good a priori alignment is provided. A large body of literature has proposed many variations in order to improve each step of the process (namely selecting, matching, rejecting, weighting and minimizing). The aim of this paper is to demonstrate how the knowledge of the shape that best fits the local geometry of each 3D point neighborhood can improve the speed and the accuracy of each of these steps. First we present the geometrical features that form the basis of this work. These low-level attributes indeed describe the neighborhood shape around each 3D point. They allow to retrieve the optimal size to analyze the neighborhoods at various scales as well as the privileged local dimension (linear, planar, or volumetric). Several variations of each step of the ICP process are then proposed and analyzed by introducing these features. Such variants are compared on real datasets with the original algorithm in order to retrieve the most efficient algorithm for the whole process. Therefore, the method is successfully applied to various 3D lidar point clouds from airborne, terrestrial, and mobile mapping systems. Improvement for two ICP steps has been noted, and we conclude that our features may not be relevant for very dissimilar object samplings. |
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| Keywords: | Point cloud Registration ICP Eigenvalues Dimensionality Neighborhood Change detection |
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