Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems
This paper presents Kimera-Multi, the first multi-robot system that (i) is robust and capable of identifying and rejecting incorrect inter and intra-robot loop closures resulting from perceptual aliasing, (ii) is fully distributed and only relies on local (peer-to-peer) communication to achieve d...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2022
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| Online Access: | https://hdl.handle.net/1721.1/145301 |
| _version_ | 1826204918065659904 |
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| author | Tian, Yulun Chang, Yun Herrera Arias, Fernando Nieto-Granda, Carlos How, Jonathan Carlone, Luca |
| author2 | Massachusetts Institute of Technology. Laboratory for Information and Decision Systems |
| author_facet | Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Tian, Yulun Chang, Yun Herrera Arias, Fernando Nieto-Granda, Carlos How, Jonathan Carlone, Luca |
| author_sort | Tian, Yulun |
| collection | MIT |
| description | This paper presents Kimera-Multi, the first multi-robot system that (i) is
robust and capable of identifying and rejecting incorrect inter and intra-robot
loop closures resulting from perceptual aliasing, (ii) is fully distributed and
only relies on local (peer-to-peer) communication to achieve distributed
localization and mapping, and (iii) builds a globally consistent
metric-semantic 3D mesh model of the environment in real-time, where faces of
the mesh are annotated with semantic labels. Kimera-Multi is implemented by a
team of robots equipped with visual-inertial sensors. Each robot builds a local
trajectory estimate and a local mesh using Kimera. When communication is
available, robots initiate a distributed place recognition and robust pose
graph optimization protocol based on a novel distributed graduated
non-convexity algorithm. The proposed protocol allows the robots to improve
their local trajectory estimates by leveraging inter-robot loop closures while
being robust to outliers. Finally, each robot uses its improved trajectory
estimate to correct the local mesh using mesh deformation techniques.
We demonstrate Kimera-Multi in photo-realistic simulations, SLAM benchmarking
datasets, and challenging outdoor datasets collected using ground robots. Both
real and simulated experiments involve long trajectories (e.g., up to 800
meters per robot). The experiments show that Kimera-Multi (i) outperforms the
state of the art in terms of robustness and accuracy, (ii) achieves estimation
errors comparable to a centralized SLAM system while being fully distributed,
(iii) is parsimonious in terms of communication bandwidth, (iv) produces
accurate metric-semantic 3D meshes, and (v) is modular and can be also used for
standard 3D reconstruction (i.e., without semantic labels) or for trajectory
estimation (i.e., without reconstructing a 3D mesh). |
| first_indexed | 2024-09-23T13:03:06Z |
| format | Article |
| id | mit-1721.1/145301 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:03:06Z |
| publishDate | 2022 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1453012023-04-10T19:25:52Z Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems Tian, Yulun Chang, Yun Herrera Arias, Fernando Nieto-Granda, Carlos How, Jonathan Carlone, Luca Massachusetts Institute of Technology. Laboratory for Information and Decision Systems This paper presents Kimera-Multi, the first multi-robot system that (i) is robust and capable of identifying and rejecting incorrect inter and intra-robot loop closures resulting from perceptual aliasing, (ii) is fully distributed and only relies on local (peer-to-peer) communication to achieve distributed localization and mapping, and (iii) builds a globally consistent metric-semantic 3D mesh model of the environment in real-time, where faces of the mesh are annotated with semantic labels. Kimera-Multi is implemented by a team of robots equipped with visual-inertial sensors. Each robot builds a local trajectory estimate and a local mesh using Kimera. When communication is available, robots initiate a distributed place recognition and robust pose graph optimization protocol based on a novel distributed graduated non-convexity algorithm. The proposed protocol allows the robots to improve their local trajectory estimates by leveraging inter-robot loop closures while being robust to outliers. Finally, each robot uses its improved trajectory estimate to correct the local mesh using mesh deformation techniques. We demonstrate Kimera-Multi in photo-realistic simulations, SLAM benchmarking datasets, and challenging outdoor datasets collected using ground robots. Both real and simulated experiments involve long trajectories (e.g., up to 800 meters per robot). The experiments show that Kimera-Multi (i) outperforms the state of the art in terms of robustness and accuracy, (ii) achieves estimation errors comparable to a centralized SLAM system while being fully distributed, (iii) is parsimonious in terms of communication bandwidth, (iv) produces accurate metric-semantic 3D meshes, and (v) is modular and can be also used for standard 3D reconstruction (i.e., without semantic labels) or for trajectory estimation (i.e., without reconstructing a 3D mesh). 2022-09-07T18:05:36Z 2022-09-07T18:05:36Z 2022 2022-09-07T17:56:54Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/145301 Tian, Yulun, Chang, Yun, Herrera Arias, Fernando, Nieto-Granda, Carlos, How, Jonathan et al. 2022. "Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems." IEEE Transactions on Robotics, 38 (4). en 10.1109/TRO.2021.3137751 IEEE Transactions on Robotics Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Tian, Yulun Chang, Yun Herrera Arias, Fernando Nieto-Granda, Carlos How, Jonathan Carlone, Luca Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems |
| title | Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems |
| title_full | Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems |
| title_fullStr | Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems |
| title_full_unstemmed | Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems |
| title_short | Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems |
| title_sort | kimera multi robust distributed dense metric semantic slam for multi robot systems |
| url | https://hdl.handle.net/1721.1/145301 |
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