Cooperative multi-robot localization under communication constraints

Authors:
Nikolas Trawny;Stergios I. Roumeliotis;Georgios B. Giannakis
Affiliations:
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN;Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN;Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

Citing 7
Cited 3

Convex Optimization

Convex Optimization
Distributed maximum a posteriori estimation for multi-robot cooperative localization

ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
SOI-KF: Distributed Kalman Filtering With Low-Cost Communications Using the Sign of Innovations

IEEE Transactions on Signal Processing
Decentralized Quantized Kalman Filtering With Scalable Communication Cost

IEEE Transactions on Signal Processing - Part I
Performance analysis of multirobot Cooperative localization

IEEE Transactions on Robotics
Optimal sensor scheduling for resource-constrained localization of mobile robot formations

IEEE Transactions on Robotics
Sequential signal encoding from noisy measurements using quantizers with dynamic bias control

IEEE Transactions on Information Theory

Decentralized localization of sparsely-communicating robot networks: a centralized-equivalent approach

IEEE Transactions on Robotics
Improving energy efficiency based on behavioral model in a swarm of cooperative foraging robots

Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Foraging swarm robots system adopting honey bee swarm for improving energy efficiency

Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper addresses the problem of cooperative localization (CL) under severe communication constraints. Specifically, we present minimum mean square error (MMSE) and maximum a posteriori (MAP) estimators that can process measurements quantized with as little as one bit per measurement. During CL, each robot quantizes and broadcasts its measurements and receives the quantized observations of its teammates. The quantization process is based on the appropriate selection of thresholds, computed using the current state estimates, that minimize the estimation error metric considered. Extensive simulations demonstrate that the proposed Iteratively-Quantized Extended Kalman filter (IQEKF) and the Iteratively Quantized MAP (IQMAP) estimator achieve performance indistinguishable of that of their real-valued counterparts (EKF and MAP, respectively) when using as few as 4 bits for quantizing each robot measurement.