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In this paper we present a new probabilistic feature-based approach to multi-hypothesis global localization and pose
 

Summary: Abstract
In this paper we present a new probabilistic feature-based
approach to multi-hypothesis global localization and pose
tracking. Hypotheses are generated using a constraint-
based search in the interpretation tree of possible local-
to-global pairings. This results in a set of robot location
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 very robust local-
ization technique which can deal with significant errors
from odometry, collisions and kidnapping. Simulation ex-
periments and first tests with a real robot 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