Motion estimation based on predator/prey vision

We present an unscented Kalman filter based state estimator for a fast moving rigid body (such as a mobile robot) endowed with two video cameras. We focus on forward velocity estimation towards the computation of standard energy cost functions for legged locomotion. Points are chosen as image features and the model of each camera is based on the traditional pinhole projection. The resulting filter's state is composed of the rigid body pose and velocities, together with a measure of depth for each tracked point. By taking inspiration from nature's large predatory and grazing mammals eye configuration, we suggest, via simulation results, a solution for the question of finding the best orientation of two cameras, between side and frontal facing, for velocity estimation in a forward moving robot.