Vehicle localization on a digital map using particles filtering

Localization is an important functionality for the navigation of intelligent vehicles. It is usually done using several kinds of sensors (proprioceptive, GPS, camera). All the data are uncertain and even momentarily unavailable (GPS in urban areas for example). A data fusion process is necessary for sensors data to compensate one each other. We propose here to combine GPS absolute localization with data computed by a vision system giving the position and orientation of the vehicle on the road. This last local information is transformed into a global reference using a map of the environment. The localization parameters are estimated using a particles filter making it possible to manage multimodal estimations (the vehicle can be on the left lane or on the right one for example). Many results have been obtained in real time and on real roads by implementing this solution in an experimental vehicle. The best standard deviation reached is 48 cm along the road axis and 8 cm along the axis normal to the road.