Find your way by observing the sun and other semantic cues - Wei-Chiu Ma; Shenlong Wang; Brubaker, Marcus A.; Fidler, Sanja; Urtasun, Raquel (2017 IEEE International Conference on Robotics and Automation (ICRA), 2017)
In this paper we present a robust, efficient and affordable approach to self-localization which requires neither GPS nor knowledge about the appearance of the world. Towards this goal, we utilize freely available cartographic maps and derive a probabilistic model that exploits semantic cues in the form of sun direction, presence of an intersection, road type, speed limit and ego-car trajectory to produce very reliable localization results. Our experimental evaluation shows that our approach can localize much faster (in terms of driving time) with less computation and more robustly than competing approaches, which ignore semantic information.