Localization and road boundary recognition in urban environments using digital street maps

Abstract

In this study, we aim to achieve autonomous navigation for robots in environments that they have not previously visited. Many of the existing methods for autonomous navigation require a map to be built beforehand, typically by manually navigating the robot. Navigation without maps, i.e., without any prior information about the environment, is very difficult. We propose to use existing digital street maps for autonomous navigation. Nowadays digital street maps (e.g., those provided by Google Maps) are widely available and used routinely. Reuse of existing maps for robots eliminates extra cost of building maps. One of the difficulties in using existing street maps is data association between a robot's observation and the map, because the physical entities that correspond to the boundary lines in the map are unknown. We address this issue by using region annotations such as roads and buildings and prior knowledge. We introduce a probabilistic framework that simultaneously estimates a robot's position and the road's boundaries.We evaluated our method in complex urban environments. Our method successfully localized in environments that includes both roadways and pedestrian walkways.