Hybrid, high-precision localisation for the mail distributing mobile robot system MOPS

This paper describes the new localisation algorithms under implementation for the mail distributing mobile robot, MOPS, of the Institute of Robotics, Swiss Federal Institute of Technology Zurich. Using geometric primitives as features, we employ consistent probabilistic feature extraction, clustering, matching and estimation of the vehicle position and orientation. The extracted features and their first-order covariance estimates are used, together with a world model, by an extended Kalman filter so as to get an optimal estimate of MOPS' current pose vector and the associated uncertainty. The line extraction consists of an initial segmentation, based on a feature-independent compactness measure in the model space, and a subsequent probabilistic clustering step. This yields a highly accurate and efficient localisation.