A multilevel fusion system for multirobot 3-D mapping using heterogeneous sensors
Operating multiple robots in an unstructured environment is challenging due to its high complexity and uncertainty. In such applications, the integration of individual maps generated by heterogeneous sensors is a critical problem, especially the fusion of sparse and dense maps. This paper proposes a...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Online Access: | https://hdl.handle.net/10356/147245 |
| _version_ | 1811678665440755712 |
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| author | Yue, Yuefeng Yang, Chule Wang, Yuanzhe Senarathne, P. G. C. N. Zhang, Jun Wen, Mingxing Wang, Danwei |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Yue, Yuefeng Yang, Chule Wang, Yuanzhe Senarathne, P. G. C. N. Zhang, Jun Wen, Mingxing Wang, Danwei |
| author_sort | Yue, Yuefeng |
| collection | NTU |
| description | Operating multiple robots in an unstructured environment is challenging due to its high complexity and uncertainty. In such applications, the integration of individual maps generated by heterogeneous sensors is a critical problem, especially the fusion of sparse and dense maps. This paper proposes a general multi-level probabilistic framework to address the integrated map fusion problem, which is independent of sensor type and SLAM algorithm employed. The key novelty of this paper is the mathematical formulation of the overall map fusion problem and the derivation of its probabilistic decomposition. The framework provides a theoretical basis for computing the relative transformations amongst robots and merging probabilistic map information. Since the maps generated by heterogeneous sensors have different physical properties, an expectation-maximization-based map-matching algorithm is proposed which automatically determines the number of voxels to be associated. Then, a time-sequential map merging strategy is developed to generate a globally consistent map. The proposed approach is evaluated in various environments with heterogeneous sensors, which demonstrates its accuracy and versatility in 3-D multirobot map fusion missions. |
| first_indexed | 2024-10-01T02:56:52Z |
| format | Journal Article |
| id | ntu-10356/147245 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:56:52Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1472452021-03-29T04:38:59Z A multilevel fusion system for multirobot 3-D mapping using heterogeneous sensors Yue, Yuefeng Yang, Chule Wang, Yuanzhe Senarathne, P. G. C. N. Zhang, Jun Wen, Mingxing Wang, Danwei School of Electrical and Electronic Engineering Engineering Robot Sensing Systems Sensor Fusion Operating multiple robots in an unstructured environment is challenging due to its high complexity and uncertainty. In such applications, the integration of individual maps generated by heterogeneous sensors is a critical problem, especially the fusion of sparse and dense maps. This paper proposes a general multi-level probabilistic framework to address the integrated map fusion problem, which is independent of sensor type and SLAM algorithm employed. The key novelty of this paper is the mathematical formulation of the overall map fusion problem and the derivation of its probabilistic decomposition. The framework provides a theoretical basis for computing the relative transformations amongst robots and merging probabilistic map information. Since the maps generated by heterogeneous sensors have different physical properties, an expectation-maximization-based map-matching algorithm is proposed which automatically determines the number of voxels to be associated. Then, a time-sequential map merging strategy is developed to generate a globally consistent map. The proposed approach is evaluated in various environments with heterogeneous sensors, which demonstrates its accuracy and versatility in 3-D multirobot map fusion missions. Accepted version 2021-03-29T03:17:46Z 2021-03-29T03:17:46Z 2020 Journal Article Yue, Y., Yang, C., Wang, Y., Senarathne, P. G. C. N., Zhang, J., Wen, M. & Wang, D. (2020). A multilevel fusion system for multirobot 3-D mapping using heterogeneous sensors. IEEE Systems Journal, 14(1), 1341-1352. https://dx.doi.org/10.1109/JSYST.2019.2927042 1932-8184 0000-0001-6628-7946 0000-0002-6548-5841 0000-0002-1683-4849 0000-0001-7406-5243 0000-0002-9751-2648 0000-0003-3400-0079 https://hdl.handle.net/10356/147245 10.1109/JSYST.2019.2927042 2-s2.0-85081701371 1 14 1341 1352 en IEEE Systems Journal © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/JSYST.2019.2927042 application/pdf |
| spellingShingle | Engineering Robot Sensing Systems Sensor Fusion Yue, Yuefeng Yang, Chule Wang, Yuanzhe Senarathne, P. G. C. N. Zhang, Jun Wen, Mingxing Wang, Danwei A multilevel fusion system for multirobot 3-D mapping using heterogeneous sensors |
| title | A multilevel fusion system for multirobot 3-D mapping using heterogeneous sensors |
| title_full | A multilevel fusion system for multirobot 3-D mapping using heterogeneous sensors |
| title_fullStr | A multilevel fusion system for multirobot 3-D mapping using heterogeneous sensors |
| title_full_unstemmed | A multilevel fusion system for multirobot 3-D mapping using heterogeneous sensors |
| title_short | A multilevel fusion system for multirobot 3-D mapping using heterogeneous sensors |
| title_sort | multilevel fusion system for multirobot 3 d mapping using heterogeneous sensors |
| topic | Engineering Robot Sensing Systems Sensor Fusion |
| url | https://hdl.handle.net/10356/147245 |
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