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Distributed maximum a posteriori estimation for multi-robot cooperative localization
Abstract
This paper presents a distributed Maximum A Posteriori (MAP) estimator for multi-robot Cooperative Localization (CL). As opposed to centralized MAP-based CL, the proposed algorithm reduces the memory and processing requirements by distributing data and computations amongst the robots. Specifically, a distributed data-allocation scheme is presented that enables robots to simultaneously process and update their local data. Additionally, a distributed Conjugate Gradient algorithm is employed that reduces the cost of computing the MAP estimates, while utilizing all available resources in the team and increasing robustness to single-point failures. Finally, a computationally efficient distributed marginalization of past robot poses is introduced for limiting the size of the optimization problem. The communication and computational complexity of the proposed algorithm is described in detail, while extensive simulation studies are presented for validating the performance of the distributed MAP estimator and comparing its accuracy to that of existing approaches.
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Nerurkar et al. propose an algorithm for cooperative localization based on distributed conjugate gradient.
“Nerurkar et al. [181] propose an algorithm for cooperative localization based on distributed conjugate gradient.”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [181]
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References 23 · Cited by 233
Research works printed but not linked: 18
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Metadata
- Authors
- Nerurkar, E. D., Roumeliotis, S. I., & Martinelli, A.
- Affiliations
- University of Minnesota (US)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Venue
- 2009 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2009-05
- Pages
- 1402-1409
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2146702612
- Cited by
- 233
- References
- 23
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 11 · 2023: 10 · 2022: 8 · 2021: 11 · 2020: 6 · 2019: 11 · 2018: 24 · 2017: 14 · 2016: 26 · 2015: 15 · 2014: 18 · 2013: 14 · 2012: 18
- FWCI
- 34.59
- 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 Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Physical Sciences › Engineering › Control and Systems Engineering › Distributed Control Multi-Agent Systems
- Keywords
- robot pose estimation, fault tolerance, communication complexity, computational complexity
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