ESTIMATION OF VEHICLE DYNAMICS FROM MONOCULAR NOISY IMAGES

This paper presents a new model-based egomotion estimation algorithm for an autonomous vehicle navigating through rough terrain. Two vehicle models available in the literature are used for egomotion estimation. The Half Vehicle Model (HVM) takes into account the bouncing 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. A feature-based approach is proposed to estimate vehicle motion parameters. An Iterated Extended Kalman Filter (IEKF) is used for recursive parameter estimation. Simulation results for both known and unknown terrain are presented.

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