Fast and robust iterative closest point

Zhang, Juyong, Yao, Yuxin and Deng, Bailin ORCID: https://orcid.org/0000-0002-0158-7670 2022. Fast and robust iterative closest point. IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (7) , pp. 3450-3466. 10.1109/TPAMI.2021.3054619

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Abstract

The Iterative Closest Point (ICP) algorithm and its variants are a fundamental technique for rigid registration between two point sets, with wide applications in different areas from robotics to 3D reconstruction. The main drawbacks for ICP are its slow convergence as well as its sensitivity to outliers, missing data, and partial overlaps. Recent work such as Sparse ICP achieves robustness via sparsity optimization at the cost of computational speed. In this paper, we propose a new method for robust registration with fast convergence. First, we show that the classical point-to-point ICP can be treated as a majorization-minimization (MM) algorithm, and propose an Anderson acceleration approach to speed up its convergence. In addition, we introduce a robust error metric based on the Welsch's function, which is minimized efficiently using the MM algorithm with Anderson acceleration. On challenging datasets with noises and partial overlaps, we achieve similar or better accuracy than Sparse ICP while being at least an order of magnitude faster. Finally, we extend the robust formulation to point-to-plane ICP, and solve the resulting problem using a similar Anderson-accelerated MM strategy. Our robust ICP methods improve the registration accuracy on benchmark datasets while being competitive in computational time.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
ISSN:	0162-8828
Date of First Compliant Deposit:	28 January 2021
Date of Acceptance:	21 January 2021
Last Modified:	22 Nov 2024 16:30
URI:	https://orca.cardiff.ac.uk/id/eprint/137995

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