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Point Set Registration for 3D Range Scans Using Fuzzy Cluster-based Metric and Efficient Global Optimization
Örebro University, School of Science and Technology.ORCID iD: 0000-0001-8119-0843
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-0334-2554
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-2953-1564
(English)In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539Article in journal (Refereed) Accepted
Abstract [en]

This study presents a new point set registration method to align 3D range scans. In our method, fuzzy clusters are utilized to represent a scan, and the registration of two given scans is realized by minimizing a fuzzy weighted sum of the distances between their fuzzy cluster centers. This fuzzy cluster-based metric has a broad basin of convergence and is robust to noise. Moreover, this metric provides analytic gradients, allowing standard gradient-based algorithms to be applied for optimization. Based on this metric, the outlier issues are addressed. In addition, for the first time in rigid point set registration, a registration quality assessment in the absence of ground truth is provided. Furthermore, given specified rotation and translation spaces, we derive the upper and lower bounds of the fuzzy cluster-based metric and develop a branch-and-bound (BnB)-based optimization scheme, which can globally minimize the metric regardless of the initialization. This optimization scheme is performed in an efficient coarse-to-fine fashion: First, fuzzy clustering is applied to describe each of the two given scans by a small number of fuzzy clusters. Then, a global search, which integrates BnB and gradient-based algorithms, is implemented to achieve a coarse alignment for the two scans. During the global search, the registration quality assessment offers a beneficial stop criterion to detect whether a good result is obtained. Afterwards, a relatively large number of points of the two scans are directly taken as the fuzzy cluster centers, and then, the coarse solution is refined to be an exact alignment using the gradient-based local convergence. Compared to existing counterparts, this optimization scheme makes a large improvementin terms of robustness and efficiency by virtue of the fuzzy cluster-based metric and the registration quality assessment. In the experiments, the registration results of several 3D range scan pairs demonstrate the accuracy and effectiveness of the proposed method, as well as its superiority to state-of-the-art registration approaches.

Place, publisher, year, edition, pages
IEEE.
Keywords [en]
Point Set Registration, Computer Vision, fuzzy clusters, registration quality assessment, 3D range scans, branch-and-bound
National Category
Computer Systems Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-80214DOI: 10.1109/TPAMI.2020.2978477OAI: oai:DiVA.org:oru-80214DiVA, id: diva2:1396976
Available from: 2020-02-26 Created: 2020-02-26 Last updated: 2020-03-10Bibliographically approved

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Point Set Registration for 3D Range Scans Using Fuzzy Cluster-based Metric and Efficient Global Optimization(4641 kB)63 downloads
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Liao, QianfangSun, DaAndreasson, Henrik

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2728293031323330 of 226
CiteExportLink to record
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