Name
Title | ||
|---|---|---|
Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems |
Abstract | ||
|---|---|---|
Multi-robot simultaneous localization and mapping (SLAM) is a crucial capability to obtain timely situational awareness over large areas. Real-world applications demand multi-robot SLAM systems to be robust to perceptual aliasing and to operate under limited communication bandwidth; moreover, it is desirable for these systems to capture semantic information to enable high-level decision-making and spatial artificial intelligence. This article presents
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, a multi-robot system that: 1) is robust and capable of identifying and rejecting incorrect inter- and intrarobot loop closures resulting from perceptual aliasing; 2) is fully distributed and only relies on local (peer-to-peer) communication to achieve distributed localization and mapping; and 3) builds a globally consistent metric-semantic 3-D mesh model of the environment in real time, where faces of the mesh are annotated with semantic labels.
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is implemented by a team of robots equipped with visual-inertial sensors. Each robot builds a local trajectory estimate and a local mesh using
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. When communication is available, robots initiate a distributed place recognition and robust pose graph optimization protocol based on a distributed graduated nonconvexity 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
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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 m per robot). The experiments show that
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: 1) outperforms the state of the art in terms of robustness and accuracy; 2) achieves estimation errors comparable to a centralized SLAM system while being fully distributed; 3) is parsimonious in terms of communication bandwidth; 4) produces accurate metric-semantic 3-D meshes; and 5) is modular and can also be used for standard 3-D reconstruction (i.e., without semantic labels) or for trajectory estimation (i.e., without reconstructing a 3-D mesh). |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TRO.2021.3137751 | IEEE Transactions on Robotics |
Keywords | DocType | Volume |
Multi-robot systems,simultaneous localization and mapping,robot vision systems | Journal | 38 |
Issue | ISSN | Citations |
4 | 1552-3098 | 1 |
PageRank | References | Authors |
0.34 | 53 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yulun Tian | 1 | 4 | 1.39 |
| Yun Chang | 2 | 3 | 0.71 |
| Fernando Herrera Arias | 3 | 1 | 0.34 |
| Carlos Nieto-Granda | 4 | 50 | 7.37 |
| Jonathan How | 5 | 1759 | 185.09 |
| Luca Carlone | 6 | 648 | 42.93 |