Map-based Priors for Localization

Abstract

Localization 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.