Map-based Priors for Localization

©2004 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 28 September-2 October 2004, Sendai, Japan.DOI: 10.1109/IROS.2004.1389732Localization from sensor measurements is a fundamental task for navigation. Particle filters are among the most promising candidates to provide a robust and realtime solution to the localization problem. They instantiate the localization problem as a Bayesian filtering problem and approximate the posterior density over location by a weighted sample set. In this paper, we introduce map-based priors for localization, using the semantic information available in maps to bias the motion model toward areas of higher probability. We show that such priors, under a particular assumption , can easily be incorporated in the particle filter by means of a pseudo likelihood. The resulting filter is more reliable and more accurate. We show experimental results on a GPS-based outdoor people tracker that illustrate the approach and highlight its potential