Towards Feature - Based Multi - Hypothesis Localization and Tracking

In this paper we present a probabilistic feature-based approach to multi-hypothesis global localization and 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 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 technique of extraordinary robustness which can deal with significant errors from odometry, collisions and kidnapping. Simulation experiments successfully demonstrate these properties at very low computational cost. The presented approach is theoretically sound which makes that the only parameter is the significance level on which all statistical decisions are taken.