Vision-based adaptive and recursive tracking of unpaved roads
We present an approach to road recognition based on Kalman filtering and EM theory resulting in a fast recursive filter that can adaptively track unpaved roads. The road model uses linear dynamic equations and an ANN front end detects road boundaries. Kalman filters construct search neighborhoods and adapt the ANN to road conditions. The road model adapts to motion dynamics using EM. Experimental results are presented.
kalman filter unpaved road recognition expectation-maximization.