Monocular Urban Localization using Street View
This addresses the problem of precise global positioning in urban settings for applications like autonomous navigation, and it is novel as the first work to use only a single camera with Street View.
The paper tackles urban localization using only a monocular camera and Google Street View, achieving sub-meter accuracy over a 3 km environment with robustness to viewpoint changes, illumination, and occlusion.
This paper presents a metric global localization in the urban environment only with a monocular camera and the Google Street View database. We fully leverage the abundant sources from the Street View and benefits from its topo-metric structure to build a coarse-to-fine positioning, namely a topological place recognition process and then a metric pose estimation by local bundle adjustment. Our method is tested on a 3 km urban environment and demonstrates both sub-meter accuracy and robustness to viewpoint changes, illumination and occlusion. To our knowledge, this is the first work that studies the global urban localization simply with a single camera and Street View.