Ego-motion and indirect road geometry estimation using night vision

The sensors present in modern premium cars deliver a wealth of information. We will in this work illustrate one way of making better use of the sensor information already present in modern premium cars. More specifically, we will show how a far infrared (FIR) camera can be used to enhance the estimates of the vehicle ego-motion and indirectly the road geometry in 3D. The FIR camera is primarily intended for pedestrian detection. The solution is obtained by solving a suitable sensor fusion problem, where we merge information from proprioceptive sensors with the FIR camera images. In order to illustrate the performance of the proposed method we have made use of measurement sequences recorded during night-time driving on rural roads in Sweden. The results illustrate that the FIR images can be used to improve the ego-motion estimation, especially during night time driving.