Recognising place under distinct weather variability, a comparison between end-to-end and metric learning approaches

Stephane Role, Demetris Marnerides, Kurt Debattista, Stefano Cavazzi, Mehrdad Dianati

Abstract: Autonomous driving requires robust and accurate real time localisation information to navigate and perform trajectory planning. Although Global Navigation Satellite Systems (GNSS) are most frequently used in this application, they are unreliable within urban environments because of multipath and non-line-of-sight errors. Alternative solutions exist that exploit rich visual content from images that can be corresponded with a stored representation, such as a map, to determine the vehicles location. However, one major cause of reduced location accuracy are variations in environmental conditions between the images captured and those stored in the representation. We tackle this issue directly by collecting a simulated and real-world dataset captured over a single route under multiple environmental conditions. We demonstrate the effectiveness of an end-to-end approach in recognising place and by extension determining vehicle location.