Distributed maximum a posteriori estimation for multi-robot cooperative localization

Esha D. Nerurkar, Stergios Roumeliotis, Agostino Martinelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

150 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Robotics and Automation, ICRA '09
Pages1402-1409
Number of pages8
DOIs
StatePublished - 2009
Event2009 IEEE International Conference on Robotics and Automation, ICRA '09 - Kobe, Japan
Duration: May 12 2009May 17 2009

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Other

Other2009 IEEE International Conference on Robotics and Automation, ICRA '09
Country/TerritoryJapan
CityKobe
Period5/12/095/17/09

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