Distributed trajectory estimation with privacy and communication constraints: A two-stage distributed Gauss-Seidel approach
Abstract
We propose a distributed algorithm to estimate the 3D trajectories of multiple cooperative robots from relative pose measurements. Our approach leverages recent results [1] which show that the maximum likelihood trajectory is well approximated by a sequence of two quadratic subproblems. The main contribution of the present work is to show that these subproblems can be solved in a distributed manner, using the distributed Gauss-Seidel (DGS) algorithm. Our approach has several advantages. It requires minimal information exchange, which is beneficial in presence of communication and privacy constraints. It has an anytime flavor: after few iterations the trajectory estimates are already accurate, and they asymptotically convergence to the centralized estimate. The DGS approach scales well to large teams, and it has a straightforward implementation. We test the approach in simulations and field tests, demonstrating its advantages over related techniques.
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An alternative to Gaussian elimination is the Gauss-Seidel approach of Choudhary et al., which implies a communication burden that is linear in the number of separators.
“An alternative approach to Gaussian elimination is the Gauss-Seidel approach of Choudhary et al. [47], which implies a communication burden which is linear in the number of separators.”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [47]
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Cites
Carlone, L., Tron, R., Daniilidis, K., & Dellaert, F. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139836 · Cited by 237 · see citing works
Self-citationOn frame and orientation localization for relative sensing networks
Piovan, G., Shames, I., Fidan, B., Bullo, F., & Anderson, B. D. O. (2013) · Automatica · doi:10.1016/j.automatica.2012.09.014 · Cited by 105 · see citing works
Multi-robot SLAM using condensed measurements
Lazaro, M. T., Paz, L. M., Pinies, P., Castellanos, J. A., & Grisetti, G. (2013) · 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2013.6696483 · Cited by 89 · see citing works
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Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2624754 · Cited by 3,596 · see citing works
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- Authors
- Choudhary, S., Carlone, L., Nieto, C., Rogers, J., Christensen, H. I., & Dellaert, F.
- Affiliations
- University of Minnesota (US)
- Venue
- 2016 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2016-05
- Pages
- 5261-5268
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2412691628
- Cited by
- 51
- References
- 3
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 8 · 2023: 9 · 2022: 2 · 2021: 3 · 2020: 3 · 2019: 1 · 2018: 4 · 2017: 4 · 2016: 3
- FWCI
- 3.42
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Control Multi-Agent Systems
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Keywords
- collaborative robots, relative pose measurement, privacy constraints, communication constraints, maximum likelihood estimation, quadratic subproblem, 3D trajectory estimation, anytime algorithms, multi-robot systems
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