Asynchronous and Parallel Distributed Pose Graph Optimization
© 2016 IEEE. We present Asynchronous Stochastic Parallel Pose Graph Optimization ($\textsc {ASAPP}$), the first asynchronous algorithm for distributed pose graph optimization (PGO) in multi-robot simultaneous localization and mapping. By enabling robots to optimize their local trajectory estimates w...
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/135250 |
| _version_ | 1826207668812906496 |
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| author | Tian, Yulun Koppel, Alec Bedi, Amrit Singh How, Jonathan P |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Tian, Yulun Koppel, Alec Bedi, Amrit Singh How, Jonathan P |
| author_sort | Tian, Yulun |
| collection | MIT |
| description | © 2016 IEEE. We present Asynchronous Stochastic Parallel Pose Graph Optimization ($\textsc {ASAPP}$), the first asynchronous algorithm for distributed pose graph optimization (PGO) in multi-robot simultaneous localization and mapping. By enabling robots to optimize their local trajectory estimates without synchronization, $\textsc {ASAPP}$ offers resiliency against communication delays and alleviates the need to wait for stragglers in the network. Furthermore, $\textsc {ASAPP}$ can be applied on the rank-restricted relaxations of PGO, a crucial class of non-convex Riemannian optimization problems that underlies recent breakthroughs on globally optimal PGO. Under bounded delay, we establish the global first-order convergence of $\textsc {ASAPP}$ using a sufficiently small stepsize. The derived stepsize depends on the worst-case delay and inherent problem sparsity, and furthermore matches known result for synchronous algorithms when there is no delay. Numerical evaluations on simulated and real-world datasets demonstrate favorable performance compared to state-of-the-art synchronous approach, and show $\textsc {ASAPP}$'s resilience against a wide range of delays in practice. |
| first_indexed | 2024-09-23T13:53:06Z |
| format | Article |
| id | mit-1721.1/135250 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:53:06Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1352502023-10-13T20:27:38Z Asynchronous and Parallel Distributed Pose Graph Optimization Tian, Yulun Koppel, Alec Bedi, Amrit Singh How, Jonathan P Massachusetts Institute of Technology. Department of Aeronautics and Astronautics © 2016 IEEE. We present Asynchronous Stochastic Parallel Pose Graph Optimization ($\textsc {ASAPP}$), the first asynchronous algorithm for distributed pose graph optimization (PGO) in multi-robot simultaneous localization and mapping. By enabling robots to optimize their local trajectory estimates without synchronization, $\textsc {ASAPP}$ offers resiliency against communication delays and alleviates the need to wait for stragglers in the network. Furthermore, $\textsc {ASAPP}$ can be applied on the rank-restricted relaxations of PGO, a crucial class of non-convex Riemannian optimization problems that underlies recent breakthroughs on globally optimal PGO. Under bounded delay, we establish the global first-order convergence of $\textsc {ASAPP}$ using a sufficiently small stepsize. The derived stepsize depends on the worst-case delay and inherent problem sparsity, and furthermore matches known result for synchronous algorithms when there is no delay. Numerical evaluations on simulated and real-world datasets demonstrate favorable performance compared to state-of-the-art synchronous approach, and show $\textsc {ASAPP}$'s resilience against a wide range of delays in practice. 2021-10-27T20:22:38Z 2021-10-27T20:22:38Z 2020 2021-04-30T13:59:19Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135250 en 10.1109/LRA.2020.3010216 IEEE Robotics and Automation Letters 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 Koppel, Alec Bedi, Amrit Singh How, Jonathan P Asynchronous and Parallel Distributed Pose Graph Optimization |
| title | Asynchronous and Parallel Distributed Pose Graph Optimization |
| title_full | Asynchronous and Parallel Distributed Pose Graph Optimization |
| title_fullStr | Asynchronous and Parallel Distributed Pose Graph Optimization |
| title_full_unstemmed | Asynchronous and Parallel Distributed Pose Graph Optimization |
| title_short | Asynchronous and Parallel Distributed Pose Graph Optimization |
| title_sort | asynchronous and parallel distributed pose graph optimization |
| url | https://hdl.handle.net/1721.1/135250 |
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