Bootstrapping computer vision and sensor fusion for absolute and relative vehicle positioning

Abstract

With the migration into automated driving for various classes of vehicles, affordable self-positioning upto at least cm accuracy is a goal to be achieved. Commonly used techniques such as GPS are either not accurate enough in their basic variant or accurate but too expensive. In addition, sufficient GPS coverage is in several cases not guaranteed. In this paper we propose positioning of a vehicle based on fusion of several sensor inputs.We consider inputs from improved GPS (with internet based corrections), inertia sensors and vehicle sensors fused with computer vision based positioning. For vision-based positioning, cameras are used for feature-based visual odometry to do relative positioning and beacon-based for absolute positioning. Visual features are brought into a dynamic map which allows sharing information among vehicles and allows us to deal with less robust feautures. This paper does not present final results, yet it is intended to share ideas that are currently being investigated and implemented.