A hierarchical framework for collaborative probabilistic semantic mapping
Performing collaborative semantic mapping is a critical challenge for cooperative robots to maintain a comprehensive contextual understanding of the surroundings. Most of the existing work either focus on single robot semantic mapping or collaborative geometry mapping. In this paper, a novel hierarc...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/147250 |
| _version_ | 1811691435525668864 |
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| author | Yue, Yufeng Zhao, Chunyang Li, Ruilin Yang, Chule Zhang, Jun Wen, Mingxing Wang, Yuanzhe Wang, Danwei |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Yue, Yufeng Zhao, Chunyang Li, Ruilin Yang, Chule Zhang, Jun Wen, Mingxing Wang, Yuanzhe Wang, Danwei |
| author_sort | Yue, Yufeng |
| collection | NTU |
| description | Performing collaborative semantic mapping is a critical challenge for cooperative robots to maintain a comprehensive contextual understanding of the surroundings. Most of the existing work either focus on single robot semantic mapping or collaborative geometry mapping. In this paper, a novel hierarchical collaborative probabilistic semantic mapping framework is proposed, where the problem is formulated in a distributed setting. The key novelty of this work is the mathematical modeling of the overall collaborative semantic mapping problem and the derivation of its probability decomposition. In the single robot level, the semantic point cloud is obtained based on heterogeneous sensor fusion model and is used to generate local semantic maps. Since the voxel correspondence is unknown in collaborative robots level, an Expectation-Maximization approach is proposed to estimate the hidden data association, where Bayesian rule is applied to perform semantic and occupancy probability update. The experimental results show the high quality global semantic map, demonstrating the accuracy and utility of 3D semantic map fusion algorithm in real missions. |
| first_indexed | 2024-10-01T06:19:51Z |
| format | Conference Paper |
| id | ntu-10356/147250 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:19:51Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1472502021-03-29T08:48:46Z A hierarchical framework for collaborative probabilistic semantic mapping Yue, Yufeng Zhao, Chunyang Li, Ruilin Yang, Chule Zhang, Jun Wen, Mingxing Wang, Yuanzhe Wang, Danwei School of Electrical and Electronic Engineering 2020 IEEE International Conference on Robotics and Automation (ICRA) Engineering Semantics Collaboration Performing collaborative semantic mapping is a critical challenge for cooperative robots to maintain a comprehensive contextual understanding of the surroundings. Most of the existing work either focus on single robot semantic mapping or collaborative geometry mapping. In this paper, a novel hierarchical collaborative probabilistic semantic mapping framework is proposed, where the problem is formulated in a distributed setting. The key novelty of this work is the mathematical modeling of the overall collaborative semantic mapping problem and the derivation of its probability decomposition. In the single robot level, the semantic point cloud is obtained based on heterogeneous sensor fusion model and is used to generate local semantic maps. Since the voxel correspondence is unknown in collaborative robots level, an Expectation-Maximization approach is proposed to estimate the hidden data association, where Bayesian rule is applied to perform semantic and occupancy probability update. The experimental results show the high quality global semantic map, demonstrating the accuracy and utility of 3D semantic map fusion algorithm in real missions. 2021-03-29T08:46:07Z 2021-03-29T08:46:07Z 2020 Conference Paper Yue, Y., Zhao, C., Li, R., Yang, C., Zhang, J., Wen, M., Wang, Y. & Wang, D. (2020). A hierarchical framework for collaborative probabilistic semantic mapping. 2020 IEEE International Conference on Robotics and Automation (ICRA), 9659-9665. https://dx.doi.org/10.1109/ICRA40945.2020.9197261 9781728173955 https://hdl.handle.net/10356/147250 10.1109/ICRA40945.2020.9197261 2-s2.0-85092703169 9659 9665 en © 2020 Institute of Electrical and Electronics Engineers (IEEE). All rights reserved. |
| spellingShingle | Engineering Semantics Collaboration Yue, Yufeng Zhao, Chunyang Li, Ruilin Yang, Chule Zhang, Jun Wen, Mingxing Wang, Yuanzhe Wang, Danwei A hierarchical framework for collaborative probabilistic semantic mapping |
| title | A hierarchical framework for collaborative probabilistic semantic mapping |
| title_full | A hierarchical framework for collaborative probabilistic semantic mapping |
| title_fullStr | A hierarchical framework for collaborative probabilistic semantic mapping |
| title_full_unstemmed | A hierarchical framework for collaborative probabilistic semantic mapping |
| title_short | A hierarchical framework for collaborative probabilistic semantic mapping |
| title_sort | hierarchical framework for collaborative probabilistic semantic mapping |
| topic | Engineering Semantics Collaboration |
| url | https://hdl.handle.net/10356/147250 |
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