Title :
Feature-based multi-hypothesis localization and tracking for mobile robots using geometric constraints
Author :
Arras, Kai O. ; Castellanos, Jose A. ; Siegwart, Roland
Author_Institution :
Autonomous Syst. Lab, Swiss Fed. Inst. of Technol. Lausanne, Switzerland
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 localization technique which can deal with significant errors from odometry, collisions and kidnapping. Simulation experiments and first tests with a real robot 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.
Keywords :
Kalman filters; computational geometry; mobile robots; navigation; position control; probability; ray tracing; state estimation; Kalman filter; constraint based search; geometric constraints; interpretation tree; mobile robots; multiple hypothesis global localization; odometry; pose tracking; position control; probabilistic feature-based localization; ray tracing; state estimation; Computational efficiency; Computational modeling; Kalman filters; Mobile robots; Probability distribution; Robot sensing systems; Robustness; Testing; Tree graphs; Uncertainty;
Conference_Titel :
Robotics and Automation, 2002. Proceedings. ICRA '02. IEEE International Conference on
Print_ISBN :
0-7803-7272-7
DOI :
10.1109/ROBOT.2002.1014734