Using multiple gaussian hypotheses to represent probability distributions for mobile robot localization
2000 (English)Conference paper (Refereed)
A new mobile robot localization technique is presented which uses multiple Gaussian hypotheses to represent the probability distribution of the robots location in the environment. A tree of hypotheses is built by the application of Bayes' rule with each new sensor mesurement. However, such a tree can grow without bound and so rules are introduced for the elimination of the least likely hypotheses from the tree and for the proper re-distribution of their probability. This technique is applied to a feature-based mobile robot localization scheme and experimental results are given demonstrating the effectiveness of the scheme.
Place, publisher, year, edition, pages
2000. 1036-1041 p.
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-52980OAI: oai:DiVA.org:kth-52980DiVA: diva2:468363
Proc. of the IEEE International Conference on Robotics and Automation (ICRA’00)
NR 201408052011-12-202011-12-20Bibliographically approved