Global Robot Localization with Random Finite Set Statistics
2010 (English)In: Fusion 2010: 13th International Conference on Information Fusion, 2010, 5711873- p.Conference paper (Refereed)
We re-examine the problem of global localization of a robot using a rigorous Bayesian framework based on the idea of random finite sets. Random sets allow us to naturally develop a complete model of the underlying problem accounting for the statistics of missed detections and of spurious/erroneously detected (potentially unmodeled) features along with the statistical models of robot hypothesis disappearance and appearance. In addition, no explicit data association is required which alleviates one of the more difficult sub-problems. Following the derivation of the Bayesian solution, we outline its first-order statistical moment approximation, the so called probability hypothesis density filter. We present a statistical estimation algorithm for the number of potential robot hypotheses consistent with the accumulated evidence and we show how such an estimate can be used to aid in re-localization of kidnapped robots. We discuss the advantages of the random set approach and examine a number of illustrative simulations.
Place, publisher, year, edition, pages
2010. 5711873- p.
Multiple-hypothesis localization, PHD filtering, Random-set-based localization, Robot localization
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-51163ScopusID: 2-s2.0-79952428121ISBN: 978-098244381-1OAI: oai:DiVA.org:kth-51163DiVA: diva2:463525
13th Conference on Information Fusion, Fusion 2010; Edinburgh; United Kingdom; 26 July 2010 through 29 July 2010
QC 201112122011-12-092011-12-092014-09-04Bibliographically approved