Summary: Robotics and Autonomous Systems 44 (2003) 4153
Feature-based multi-hypothesis localization and
tracking using geometric constraints
Kai O. Arrasa,, José A. Castellanosb, Martin Schilta, Roland Siegwarta
a Autonomous Systems Lab, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015 Lausanne, Switzerland
b Robotics and Real-Time Group, Centro Politecnico Superior, Universidad de Zaragoza, E-50015 Zaragoza, Spain
Mobile robot localization deals with uncertain sensory information as well as uncertain data association. In this paper
we present a probabilistic feature-based approach to global localization and pose tracking which explicitly addresses both
problems. Location hypotheses are represented as Gaussian distributions. Hypotheses are found by a search in the tree of
possible local-to-global feature associations, given a local map of observed features and a global map of the environment.
During tree traversal, several types of geometric constraints are used to determine statistically feasible associations. As soon
as hypotheses are available, they are tracked using the same constraint-based technique. Track splitting is performed when
location ambiguity arises from uncertainties and sensing. This yields a very robust localization technique which can deal with
significant errors from odometry, collisions and kidnapping. Experiments in simulation and with a real robot demonstrate
these properties at low computational costs.
© 2003 Elsevier Science B.V. All rights reserved.
Keywords: Mobile robot localization; Multi-hypothesis tracking; Geometric constraints; Kalman filtering; Data association
Kalman filter-based position tracking with geo-