Abstract-Current GPS-based devices have difficulty localizing in cases where the GPS signal is unavailable or insufficiently accurate. This paper presents an algorithm for localizing a vehicle on an arbitrary road network using vision, road curvature estimates, or a combination of both. The method uses an extension of topometric localization, which is a hybrid between topological and metric localization. The extension enables localization on a network of roads rather than just a single, non-branching route. The algorithm, which does not rely on GPS, is able to localize reliably in situations where GPSbased devices fail, including "urban canyons" in downtown areas and along ambiguous routes with parallel roads. We demonstrate the algorithm experimentally on several road networks in urban, suburban, and highway scenarios. We also evaluate the road curvature descriptor and show that it is effective when imagery is sparsely available.
The work of Xu et al. REF is complementary to our work that uses computer vision to localize a car on a network of roads by creating a road curvature descriptor.
Topometric localization on a road network
2014 IEEE/RSJ International Conference on Intelligent Robots and Systems
2014 IEEE/RSJ International Conference on Intelligent Robots and Systems
Computer Science