Find your Way by Observing the Sun and Other Semantic Cues
This provides an affordable and efficient solution for autonomous vehicles or navigation systems in GPS-denied environments, though it appears incremental by building on semantic mapping techniques.
The paper tackles the problem of self-localization without GPS or prior visual knowledge by using semantic cues from cartographic maps, such as sun direction and road features, to achieve faster and more robust localization than existing methods.
In this paper we present a robust, efficient and affordable approach to self-localization which does not require 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 as well as the ego-car trajectory in order 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.