Mutual localization in multi-robot systems using anonymous relative measurements

Antonio Franchi*, Giuseppe Oriolo, Paolo Stegagno

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

35 Citations (Scopus)

Abstract

We propose a decentralized method to perform mutual localization in multi-robot systems using anonymous relative measurements, i.e. measurements that do not include the identity of the measured robot. This is a challenging and practically relevant operating scenario that has received little attention in the literature. Our mutual localization algorithm includes two main components: a probabilistic multiple registration stage, which provides all data associations that are consistent with the relative robot measurements and the current belief, and a dynamic filtering stage, which incorporates odometric data into the estimation process. The design of the proposed method proceeds from a detailed formal analysis of the implications of anonymity on the mutual localization problem. Experimental results on a team of differential-drive robots illustrate the effectiveness of the approach, and in particular its robustness against false positives and negatives that may affect the robot measurement process. We also provide an experimental comparison that shows how the proposed method outperforms more classical approaches that may be designed building on existing techniques. The source code of the proposed method is available within the MLAM ROS stack.

Original languageEnglish
Pages (from-to)1302-1322
Number of pages21
JournalInternational journal of robotics research
Volume32
Issue number11
DOIs
Publication statusPublished - Sep 2013
Externally publishedYes

Keywords

  • Anonymous measurements
  • EKF
  • Localization
  • Mobile robots
  • Multi-robot localization
  • Particle filters
  • Range finders
  • RANSAC
  • Relative measurements
  • Unknown data-association

Fingerprint Dive into the research topics of 'Mutual localization in multi-robot systems using anonymous relative measurements'. Together they form a unique fingerprint.

Cite this