Real-time bounded-error pose estimation for road vehicles using vision

Ashley Napier, Gabe Sibley, Paul Newman

Abstract: This paper is about online, constant-time pose estimation for road vehicles. We exploit both the state of the art in vision based SLAM and the wide availability of overhead imagery of road networks. We show that by formulating the pose estimation problem in a relative sense, we can estimate the vehicle pose in real-time and bound its absolute error by using overhead image priors. We demonstrate our technique on data gathered from a stereo pair on a vehicle traveling at 40 kph through urban streets. Crucially our method has no dependence on infrastructure, needs no workspace modification, is not dependent on GPS reception, requires only a single stereo pair and runs on an every day laptop.