Vision-based Robot Localization Across Seasons and in Remote Locations

This paper studies the problem of GPS-denied unmanned ground vehicle (UGV) localization by matching ground images to a satellite map. We examine the realistic, but particularly challenging problem of navigation in remote areas using maps that may correspond to a different season of the year. The problem is difficult due to the limited UGV sensor horizon, the drastic shift in perspective between ground and aerial views, the absence of discriminative features in the environment due to the remote location, and the high variation in appearance of the satellite map caused by the change in seasons. We present an approach to image matching using semantic information that is invariant to seasonal change. This semantics-based matching is incorporated into a particle filter framework and successful localization of the ground vehicle is demonstrated for satellite maps captured in summer, spring, and winter.