Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/118748
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Type: Journal article
Title: Practical optimal registration of terrestrial LiDAR scan pairs
Author: Cai, Z.
Chin, T.
Bustos, A.
Schindler, K.
Citation: ISPRS Journal of Photogrammetry and Remote Sensing, 2019; 147:118-131
Publisher: Lippincott Williams & Wilkins
Issue Date: 2019
ISSN: 0924-2716
Statement of
Responsibility: 
Zhipeng Cai, Tat-Jun Chin, Alvaro Parra Bustos, Konrad Schindler
Abstract: Point cloud registration is a fundamental problem in 3D scanning. In this paper, we address the frequent special case of registering terrestrial LiDAR scans (or, more generally, levelled point clouds). Many current solutions still rely on the Iterative Closest Point (ICP) method or other heuristic procedures, which require good initializations to succeed and/or provide no guarantees of success. On the other hand, exact or optimal registration algorithms can compute the best possible solution without requiring initializations; however, they are currently too slow to be practical in realistic applications. Existing optimal approaches ignore the fact that in routine use the relative rotations between scans are constrained to the azimuth, via the built-in level compensation in LiDAR scanners. We propose a novel, optimal and computationally efficient registration method for this 4DOF scenario. Our approach operates on candidate 3D keypoint correspondences, and contains two main steps: (1) a deterministic selection scheme that significantly reduces the candidate correspondence set in a way that is guaranteed to preserve the optimal solution; and (2) a fast branch-and-bound (BnB) algorithm with a novel polynomial-time subroutine for 1D rotation search, that quickly finds the optimal alignment for the reduced set. We demonstrate the practicality of our method on realistic point clouds from multiple LiDAR surveys.
Keywords: Point cloud registration; exact optimization; branch-and-bound
Rights: © 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
RMID: 0030108757
DOI: 10.1016/j.isprsjprs.2018.11.016
Appears in Collections:Computer Science publications

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