Distributed Certifiably Correct Range-Aided SLAM
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910941795319808 |
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| author | Thoms, Alexander Papalia, Alan Velasquez, Jared Rosen, David M. Narasimhan, Sriram |
| author_facet | Thoms, Alexander Papalia, Alan Velasquez, Jared Rosen, David M. Narasimhan, Sriram |
| contents | Reliable simultaneous localization and mapping (SLAM) algorithms are necessary for safety-critical autonomous navigation. In the communication-constrained multi-agent setting, navigation systems increasingly use point-to-point range sensors as they afford measurements with low bandwidth requirements and known data association. The state estimation problem for these systems takes the form of range-aided (RA) SLAM. However, distributed algorithms for solving the RA-SLAM problem lack formal guarantees on the quality of the returned estimate. To this end, we present the first distributed algorithm for RA-SLAM that can efficiently recover certifiably globally optimal solutions. Our algorithm, distributed certifiably correct RA-SLAM (DCORA), achieves this via the Riemannian Staircase method, where computational procedures developed for distributed certifiably correct pose graph optimization are generalized to the RA-SLAM problem. We demonstrate DCORA's efficacy on real-world multi-agent datasets by achieving absolute trajectory errors comparable to those of a state-of-the-art centralized certifiably correct RA-SLAM algorithm. Additionally, we perform a parametric study on the structure of the RA-SLAM problem using synthetic data, revealing how common parameters affect DCORA's performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03192 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributed Certifiably Correct Range-Aided SLAM Thoms, Alexander Papalia, Alan Velasquez, Jared Rosen, David M. Narasimhan, Sriram Robotics Reliable simultaneous localization and mapping (SLAM) algorithms are necessary for safety-critical autonomous navigation. In the communication-constrained multi-agent setting, navigation systems increasingly use point-to-point range sensors as they afford measurements with low bandwidth requirements and known data association. The state estimation problem for these systems takes the form of range-aided (RA) SLAM. However, distributed algorithms for solving the RA-SLAM problem lack formal guarantees on the quality of the returned estimate. To this end, we present the first distributed algorithm for RA-SLAM that can efficiently recover certifiably globally optimal solutions. Our algorithm, distributed certifiably correct RA-SLAM (DCORA), achieves this via the Riemannian Staircase method, where computational procedures developed for distributed certifiably correct pose graph optimization are generalized to the RA-SLAM problem. We demonstrate DCORA's efficacy on real-world multi-agent datasets by achieving absolute trajectory errors comparable to those of a state-of-the-art centralized certifiably correct RA-SLAM algorithm. Additionally, we perform a parametric study on the structure of the RA-SLAM problem using synthetic data, revealing how common parameters affect DCORA's performance. |
| title | Distributed Certifiably Correct Range-Aided SLAM |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.03192 |