Particle filtering for map-aided localization in sparse GPS environments

This study presents the PosteriorPose algorithm, a Bayesian particle filtering approach for augmenting GPS and inertial navigation solutions with vision-based measurements of nearby lanes and stoplines referenced against a known map of environmental features. These relative measurements are shown to improve the quality of the navigation solution when GPS is available, and they are shown to keep the navigation solution converged in extended GPS blackouts. Measurements are incorporated with careful hypothesis testing and error modeling to account for non-Gaussian errors committed by vision-based detection algorithms. The PosteriorPose algorithm is implemented and validated in real-time on Cornell University´s 2007 DARPA Urban Challenge entry; experimental data is presented showing the algorithm outperforming a tightly- coupled GPS/inertial navigation solution both in full GPS coverage and in an extended GPS blackout.