PeRL STUDIES AUTONOMOUS NAVIGATION & MAPPING FOR MOBILE ROBOTS IN A PRIORI UNKNOWN ENVIRONMENTS.
A decentralised particle filtering algorithm for multi-target tracking across multiple flight vehicles
Summary
Abstract
This paper presents a decentralised particle filtering algorithm that enables multiple vehicles to jointly track 3D features under limited communication bandwidth. This algorithm, applied within a decentralised data fusion (DDF) framework, deals with correlated estimation errors due to common past information when fusing two discrete particle sets. Our solution is to transform the particles into Gaussian mixture models (GMMs) for communication and fusion. Not only can decentralised fusion be approximated by GMMs, but this representation also provides summaries of the particle set. Less bandwidth per communication step is required to communicate a GMM than the particle set itself hence conversion to GMMs for communication is an advantage. Real airborne data is used to demonstrate the accuracy of our decentralised particle filtering algorithm for airborne tracking and mapping
Bibtex entry
@INPROCEEDINGS { long-2006a,
AUTHOR = { Lee-Ling Ong and Upcroft, B. and Bailey, T. and Ridley, M. and Sukkarieh, S. and Durrant-Whyte, H. },
TITLE = { A decentralised particle filtering algorithm for multi-target tracking across multiple flight vehicles },
BOOKTITLE = { Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems },
YEAR = { 2006 },
MONTH = { October },
ADDRESS = { Beijing, China },
PAGES = { 4539--4544 },
DOI = { 10.1109/IROS.2006.282155 },
}