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In this paper we present a probabilistic feature-based ap-proach to multi-hypothesis global localization and track-
 

Summary: Abstract
In this paper we present a probabilistic feature-based ap-
proach to multi-hypothesis global localization and track-
ing. Hypotheses are generated using a constraint-based
search in the interpretation tree of possible local-to-glo-
bal-pairings. This results in a set of continuously located
position hypotheses of unbounded accuracy. For tracking,
the same constraint-based technique is used. It performs
track splitting as soon as location ambiguities arise from
uncertainties and sensing. This yields a localization tech-
nique of extraordinary robustness which can deal with
significant errors from odometry, collisions and kidnap-
ping. Simulation experiments successfully demonstrate
these properties at very low computational cost. The pre-
sented approach is theoretically sound which makes that
the only parameter is the significance level on which
all statistical decisions are taken.
1. Introduction
Kalman filter-based position tracking with geometric fea-
tures has been proven to be a very powerful localization

  

Source: Arras, Kai O. - Institut für Informatik, Albert-Ludwigs-Universität Freiburg

 

Collections: Computer Technologies and Information Sciences