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Revisiting Distribution-Based Registration Methods
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems (AASS))
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-2953-1564
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0001-8658-2985
Department of Computer Science, University College London, London, England.
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2023 (English)In: 2023 European Conference on Mobile Robots (ECMR) / [ed] Marques, L.; Markovic, I., IEEE , 2023, p. 43-48Conference paper, Published paper (Refereed)
Abstract [en]

Normal Distribution Transformation (NDT) registration is a fast, learning-free point cloud registration algorithm that works well in diverse environments. It uses the compact NDT representation to represent point clouds or maps as a spatial probability function that models the occupancy likelihood in an environment. However, because of the grid discretization in NDT maps, the global minima of the registration cost function do not always correlate to ground truth, particularly for rotational alignment. In this study, we examined the NDT registration cost function in-depth. We evaluated three modifications (Student-t likelihood function, inflated covariance/heavily broadened likelihood curve, and overlapping grid cells) that aim to reduce the negative impact of discretization in classical NDT registration. The first NDT modification improves likelihood estimates for matching the distributions of small population sizes; the second modification reduces discretization artifacts by broadening the likelihood tails through covariance inflation; and the third modification achieves continuity by creating the NDT representations with overlapping grid cells (without increasing the total number of cells). We used the Pomerleau Dataset evaluation protocol for our experiments and found significant improvements compared to the classic NDT D2D registration approach (27.7% success rate) using the registration cost functions "heavily broadened likelihood NDT" (HBL-NDT) (34.7% success rate) and "overlapping grid cells NDT" (OGC-NDT) (33.5% success rate). However, we could not observe a consistent improvement using the Student-t likelihood-based registration cost function (22.2% success rate) over the NDT P2D registration cost function (23.7% success rate). A comparative analysis with other state-of-art registration algorithms is also presented in this work. We found that HBL-NDT worked best for easy initial pose difficulties scenarios making it suitable for consecutive point cloud registration in SLAM application.

Place, publisher, year, edition, pages
IEEE , 2023. p. 43-48
Series
European Conference on Mobile Robots, ISSN 2639-7919, E-ISSN 2767-8733
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:oru:diva-109681DOI: 10.1109/ECMR59166.2023.10256416ISI: 001082260500007Scopus ID: 2-s2.0-8517439971ISBN: 9798350307047 (electronic)ISBN: 9798350307054 (print)OAI: oai:DiVA.org:oru-109681DiVA, id: diva2:1812049
Conference
11th European Conference on Mobile Robots (ECMR 2023), Coimbra, Portugal, September 4-7, 2023
Funder
EU, Horizon 2020, 858101Available from: 2023-11-15 Created: 2023-11-15 Last updated: 2023-11-15Bibliographically approved

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Gupta, HimanshuAndreasson, HenrikMagnusson, MartinLilienthal, Achim J.

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CiteExportLink to record
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