(Also cross-referenced as CAR-TR-692)

This paper presents a new model-based egol lotion estimation

algorithm for an autonomous vehicle navigating through rough terrain. Due to the uneven terrain, the vehicle undergoes bouncing, pitch and roll motion. To reliably accomplish other tasks such a s tracking and obstacle avoidance using visual inputs, it is essential to consider these disturbances. In this paper, two vehicle models available in the literature are used for egomotion estimation. The Half Vehicle Model (HVM) takes into account the bou ncing and pitch motion of the vehicle, and the Full Vehicle Model (FVM) also considers the roll motion. The dynamics of the vehicle are formulated using standard equations of motion. Assuming that depth information is known for some landmarks in the scene (e.g., obtained from a laser range finder), a feature-based approach is proposed to estimate vehicle motion parameters such as the vertical movement of the center of mass and the instantaneous angular velocity. An Iterated Extended Kalman Filter (IEKF) is used for recursive parameter estimation. Simulation results for both known and unknown terrain are presented.