Multi-sensor data fusion for autonomous vehicle navigation through adaptive particle filter

In urban environment, we need accurate and precise estimation of vehicle state for real time navigation and control. This paper presents an architecture to fuse different data from onboard sensors to estimate the vehicle state when observations are noisy. We are trying to compensate the GPS errors by data fusion from different sensors in a probabilistic way. A particle filter with joint observation model has been proposed to real timely estimate the vehicle state. An adaptive joint observation model has been developed to fuse different observations according to accuracy and reliability of the corresponding sensor. Finally a navigation architecture has been proposed for fully autonomous driving with dynamic obstacles. Experiments with real vehicle show the proposed method is able to estimate the vehicle state precisely when the individual observations fail to be enough accurate.