Collective localization: a distributed Kalman filter approach to localization of groups of mobile robots

Stergios I. Roumeliotis, George A. Bekey

Research output: Contribution to journalConference articlepeer-review

120 Scopus citations

Abstract

This paper presents a new approach to the cooperative localization problem, namely collective localization. A group of M robots is viewed as a single system composed of robots that carry, in general, different sensors and have different positioning capabilities. A single Kalman filter is formulated to estimate the position and orientation of all the members of the group. This centralized schema is capable of fusing information provided by the sensors distributed on the individual robots while accommodating independencies and interdependencies among the collected data. In order to allow for distributed processing, the equations of the centralized Kalman filter are treated so that this filter can be decomposed in M modified Kalman filters each running on a separate robot. The collective localization algorithm is applied to a group of 3 robots and the improvement in localization accuracy is presented.

Original languageEnglish (US)
Pages (from-to)2958-2965
Number of pages8
JournalProceedings - IEEE International Conference on Robotics and Automation
Volume3
StatePublished - Dec 3 2000
EventICRA 2000: IEEE International Conference on Robotics and Automation - San Francisco, CA, USA
Duration: Apr 24 2000Apr 28 2000

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