Localization with Sliding Window Factor Graphs on Third-Party Maps for Automated Driving

Abstract

Localizing a vehicle in a map is essential for automated driving and various other robotic applications. This paper addresses the problem of vehicle localization in urban environments. Our approach performs a graph-based sliding window optimization over a set of recent landmark and odometry measurements for fast and accurate vehicle localization on third-party maps. Our work incorporates landmark priors from third-party maps into the estimation problem and shows how to exploit the sliding window formulation for revising data associations. We describe how to construct our factor graph and derive its necessary factors to model the information from the map as a prior over the landmark detections. We implemented our approach on an automated car and thoroughly tested it on real-world data. The experiments suggest that the approach provides highly accurate pose estimates, is fast enough for automated driving applications, and outperforms localization using particle filters.