Bayesian Sensor Fusion of GNSS and Camera With Outlier Adaptation for Vehicle Positioning

In this paper we develop a method for vehicle positioning based on global navigation satellite system (GNSS) and camera information. Both GNSS and camera measurements have noise characteristics that vary in time. As a result, the measurements can abruptly change from reliable to unreliable from one time step to another. To adapt to the changing noise levels and hence improve positioning performance, we combine GNSS information with measurements from a forward looking camera, a steering-wheel angle sensor, wheel-speed sensors, and optionally an inertial sensor. We pose the estimation problem in an interacting multiple-model (IMM) setting and use Bayes recursion to choose the best combination of the estimators. In a simulation study, we compare vehicle models with varying complexity, and on a real road segment we show that the proposed method can accurately adjust to changing noise conditions.