Benchmarking Classical and Learning-Based Multibeam Point Cloud Registration ...
Deep learning has shown promising results for multiple 3D point cloud registration datasets. However, in the underwater domain, most registration of multibeam echo-sounder (MBES) point cloud data are still performed using classical methods in the iterative closest point (ICP) family. In this work, w...
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Online Access: | https://dx.doi.org/10.48550/arxiv.2405.06279 https://arxiv.org/abs/2405.06279 |
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ftdatacite:10.48550/arxiv.2405.06279 2024-09-09T19:10:21+00:00 Benchmarking Classical and Learning-Based Multibeam Point Cloud Registration ... Ling, Li Zhang, Jun Bore, Nils Folkesson, John Wåhlin, Anna 2024 https://dx.doi.org/10.48550/arxiv.2405.06279 https://arxiv.org/abs/2405.06279 unknown arXiv Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Computer Vision and Pattern Recognition cs.CV Robotics cs.RO FOS Computer and information sciences Article article Preprint CreativeWork 2024 ftdatacite https://doi.org/10.48550/arxiv.2405.06279 2024-06-17T09:30:06Z Deep learning has shown promising results for multiple 3D point cloud registration datasets. However, in the underwater domain, most registration of multibeam echo-sounder (MBES) point cloud data are still performed using classical methods in the iterative closest point (ICP) family. In this work, we curate and release DotsonEast Dataset, a semi-synthetic MBES registration dataset constructed from an autonomous underwater vehicle in West Antarctica. Using this dataset, we systematically benchmark the performance of 2 classical and 4 learning-based methods. The experimental results show that the learning-based methods work well for coarse alignment, and are better at recovering rough transforms consistently at high overlap (20-50%). In comparison, GICP (a variant of ICP) performs well for fine alignment and is better across all metrics at extremely low overlap (10%). To the best of our knowledge, this is the first work to benchmark both learning-based and classical registration methods on an AUV-based MBES ... : Accepted at ICRA 2024 (IEEE International Conference on Robotics and Automation 2024) ... Article in Journal/Newspaper Antarc* Antarctica West Antarctica DataCite West Antarctica |
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topic |
Computer Vision and Pattern Recognition cs.CV Robotics cs.RO FOS Computer and information sciences |
spellingShingle |
Computer Vision and Pattern Recognition cs.CV Robotics cs.RO FOS Computer and information sciences Ling, Li Zhang, Jun Bore, Nils Folkesson, John Wåhlin, Anna Benchmarking Classical and Learning-Based Multibeam Point Cloud Registration ... |
topic_facet |
Computer Vision and Pattern Recognition cs.CV Robotics cs.RO FOS Computer and information sciences |
description |
Deep learning has shown promising results for multiple 3D point cloud registration datasets. However, in the underwater domain, most registration of multibeam echo-sounder (MBES) point cloud data are still performed using classical methods in the iterative closest point (ICP) family. In this work, we curate and release DotsonEast Dataset, a semi-synthetic MBES registration dataset constructed from an autonomous underwater vehicle in West Antarctica. Using this dataset, we systematically benchmark the performance of 2 classical and 4 learning-based methods. The experimental results show that the learning-based methods work well for coarse alignment, and are better at recovering rough transforms consistently at high overlap (20-50%). In comparison, GICP (a variant of ICP) performs well for fine alignment and is better across all metrics at extremely low overlap (10%). To the best of our knowledge, this is the first work to benchmark both learning-based and classical registration methods on an AUV-based MBES ... : Accepted at ICRA 2024 (IEEE International Conference on Robotics and Automation 2024) ... |
format |
Article in Journal/Newspaper |
author |
Ling, Li Zhang, Jun Bore, Nils Folkesson, John Wåhlin, Anna |
author_facet |
Ling, Li Zhang, Jun Bore, Nils Folkesson, John Wåhlin, Anna |
author_sort |
Ling, Li |
title |
Benchmarking Classical and Learning-Based Multibeam Point Cloud Registration ... |
title_short |
Benchmarking Classical and Learning-Based Multibeam Point Cloud Registration ... |
title_full |
Benchmarking Classical and Learning-Based Multibeam Point Cloud Registration ... |
title_fullStr |
Benchmarking Classical and Learning-Based Multibeam Point Cloud Registration ... |
title_full_unstemmed |
Benchmarking Classical and Learning-Based Multibeam Point Cloud Registration ... |
title_sort |
benchmarking classical and learning-based multibeam point cloud registration ... |
publisher |
arXiv |
publishDate |
2024 |
url |
https://dx.doi.org/10.48550/arxiv.2405.06279 https://arxiv.org/abs/2405.06279 |
geographic |
West Antarctica |
geographic_facet |
West Antarctica |
genre |
Antarc* Antarctica West Antarctica |
genre_facet |
Antarc* Antarctica West Antarctica |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.48550/arxiv.2405.06279 |
_version_ |
1809825245915250688 |