Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/118748
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dc.contributor.authorCai, Z.en
dc.contributor.authorChin, T.en
dc.contributor.authorBustos, A.en
dc.contributor.authorSchindler, K.en
dc.date.issued2019en
dc.identifier.citationISPRS Journal of Photogrammetry and Remote Sensing, 2019; 147:118-131en
dc.identifier.issn0924-2716en
dc.identifier.issn1872-8235en
dc.identifier.urihttp://hdl.handle.net/2440/118748-
dc.description.abstractPoint 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.en
dc.description.statementofresponsibilityZhipeng Cai, Tat-Jun Chin, Alvaro Parra Bustos, Konrad Schindleren
dc.language.isoenen
dc.publisherLippincott Williams & Wilkinsen
dc.rights© 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.en
dc.subjectPoint cloud registration; exact optimization; branch-and-bounden
dc.titlePractical optimal registration of terrestrial LiDAR scan pairsen
dc.typeJournal articleen
dc.identifier.doi10.1016/j.isprsjprs.2018.11.016en
dc.relation.granthttp://purl.org/au-research/grants/arc/DP160103490en
pubs.publication-statusPublisheden
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