Comparison of Point Cloud Registration Algorithms for Mixed-Reality Cross-Device Global Localization
State-of-the-art approaches for localization and mapping are based on local features in images. Along with these features, modern augmented and mixed-reality devices enable building a mesh of the surrounding space. Using this mesh map, we can solve the problem of cross-device localization. This appr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/14/3/149 |
| _version_ | 1797611143533953024 |
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| author | Alexander Osipov Mikhail Ostanin Alexandr Klimchik |
| author_facet | Alexander Osipov Mikhail Ostanin Alexandr Klimchik |
| author_sort | Alexander Osipov |
| collection | DOAJ |
| description | State-of-the-art approaches for localization and mapping are based on local features in images. Along with these features, modern augmented and mixed-reality devices enable building a mesh of the surrounding space. Using this mesh map, we can solve the problem of cross-device localization. This approach is independent of the type of feature descriptors and SLAM used onboard the AR/MR device. The mesh could be reduced to the point cloud that only takes vertices. We analyzed and compared different point cloud registration methods applicable to the problem. In addition, we proposed a new pipeline Feature Inliers Graph Registration Approach (FIGRA) for the co-localization of AR/MR devices using point clouds. The comparative analysis of Go-ICP, Bayesian-ICP, FGR, Teaser++, and FIGRA shows that feature-based methods are more robust and faster than ICP-based methods. Through an in-depth comparison of the feature-based methods with the usual fast point feature histogram and the new weighted height image descriptor, we found that FIGRA has a better performance due to its effective graph-theoretic base. The proposed pipeline allows one to match point clouds in complex real scenarios with low overlap and sparse point density. |
| first_indexed | 2024-03-11T06:24:38Z |
| format | Article |
| id | doaj.art-fdb79def787944e785e6ecf5966885af |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-11T06:24:38Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-fdb79def787944e785e6ecf5966885af2023-11-17T11:43:47ZengMDPI AGInformation2078-24892023-02-0114314910.3390/info14030149Comparison of Point Cloud Registration Algorithms for Mixed-Reality Cross-Device Global LocalizationAlexander Osipov0Mikhail Ostanin1Alexandr Klimchik2Institute of Robotics and Computer Vision, Innopolis University, 420500 Innopolis, RussiaInstitute of Robotics and Computer Vision, Innopolis University, 420500 Innopolis, RussiaSchool of Computer Science, University of Lincoln, Lincoln LN6 7TS, UKState-of-the-art approaches for localization and mapping are based on local features in images. Along with these features, modern augmented and mixed-reality devices enable building a mesh of the surrounding space. Using this mesh map, we can solve the problem of cross-device localization. This approach is independent of the type of feature descriptors and SLAM used onboard the AR/MR device. The mesh could be reduced to the point cloud that only takes vertices. We analyzed and compared different point cloud registration methods applicable to the problem. In addition, we proposed a new pipeline Feature Inliers Graph Registration Approach (FIGRA) for the co-localization of AR/MR devices using point clouds. The comparative analysis of Go-ICP, Bayesian-ICP, FGR, Teaser++, and FIGRA shows that feature-based methods are more robust and faster than ICP-based methods. Through an in-depth comparison of the feature-based methods with the usual fast point feature histogram and the new weighted height image descriptor, we found that FIGRA has a better performance due to its effective graph-theoretic base. The proposed pipeline allows one to match point clouds in complex real scenarios with low overlap and sparse point density.https://www.mdpi.com/2078-2489/14/3/149indoor collaborative localizationaugmented and mixed-reality devicespoint cloud registrationcomparison |
| spellingShingle | Alexander Osipov Mikhail Ostanin Alexandr Klimchik Comparison of Point Cloud Registration Algorithms for Mixed-Reality Cross-Device Global Localization Information indoor collaborative localization augmented and mixed-reality devices point cloud registration comparison |
| title | Comparison of Point Cloud Registration Algorithms for Mixed-Reality Cross-Device Global Localization |
| title_full | Comparison of Point Cloud Registration Algorithms for Mixed-Reality Cross-Device Global Localization |
| title_fullStr | Comparison of Point Cloud Registration Algorithms for Mixed-Reality Cross-Device Global Localization |
| title_full_unstemmed | Comparison of Point Cloud Registration Algorithms for Mixed-Reality Cross-Device Global Localization |
| title_short | Comparison of Point Cloud Registration Algorithms for Mixed-Reality Cross-Device Global Localization |
| title_sort | comparison of point cloud registration algorithms for mixed reality cross device global localization |
| topic | indoor collaborative localization augmented and mixed-reality devices point cloud registration comparison |
| url | https://www.mdpi.com/2078-2489/14/3/149 |
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