Intelligent vehicle localization using GPS, compass, and machine vision

Intelligent vehicles require accurate localization relative to a map to ensure safe travel. GPS sensors are among the most useful sensors for outdoor localization, but they still suffer from noise due to weather conditions, tree cover, and surrounding buildings or other structures. In this paper, to improve localization accuracy when GPS fails, we propose a sequential state estimation method that fuses data from a GPS device, an electronic compass, a video camera, and wheel encoders using a particle filter. We process images from the camera using a color histogram-based method to identify the road and non-road regions in the field of view in front of the vehicle. In two experiments, in simulation and on a real vehicle, we demonstrate that, compared to a standard extended Kalman filter not using image data, our method significantly improves lateral localization error during periods of GPS inaccuracy.