Recursive decentralized localization for multi-robot systems with asynchronous pairwise communication

This paper provides a fully decentralized algorithm for collaborative localization based on the extended Kalman filter. The major challenge in decentralized collaborative localization is to track inter-robot dependencies, which is particularly difficult when sustained synchronous communication betwe...

Full description

Saved in:
Bibliographic Details
Published in:The International journal of robotics research 2018-09, Vol.37 (10), p.1152-1167
Main Authors: Luft, Lukas, Schubert, Tobias, Roumeliotis, Stergios I., Burgard, Wolfram
Format: Article
Language:English
Subjects:
Algorithms
Collaboration
Communication
Extended Kalman filter
Localization
Mathematical models
Multiple robots
Robots
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper provides a fully decentralized algorithm for collaborative localization based on the extended Kalman filter. The major challenge in decentralized collaborative localization is to track inter-robot dependencies, which is particularly difficult when sustained synchronous communication between the robots cannot be guaranteed. Current approaches suffer from the need for particular communication schemes, extensive bookkeeping of measurements, overly conservative assumptions, or the restriction to specific measurement models. This paper introduces a localization algorithm that is able to approximate the inter-robot correlations while fulfilling all of the following conditions: communication is limited to two robots that obtain a relative measurement, the algorithm is recursive in the sense that it does not require storage of measurements and each robot maintains only the latest estimate of its own pose, and it supports generic measurement models. The fact that the proposed approach can handle these particularly difficult conditions ensures that it is applicable to a wide range of multi-robot scenarios. We provide mathematical details on our approximation. Extensive experiments carried out using real-world datasets demonstrate the improved performance of our method compared with several existing approaches.
ISSN:0278-3649
1741-3176
DOI:10.1177/0278364918760698