Vehicle ego-motion estimation with geometric algebra

Abstract

A method for estimating ego-motion with vehicle mounted stereo cameras is presented. This approach is based on finding corresponding features in stereo images and tracking them between succeeding stereo frames. Our approach estimates stereo ego-motion with geometric algebra techniques. Starting with a simple linear estimate of the ego-motion, estimates for the motion and reconstructed positions of features in space are updated incrementally. With simple algebraic operations it is possible to improve towards a high precision motion estimate. Accuracy of our algorithm has been measured with simulated data. These tests show improvement in motion estimation accuracy. Random sample consensus is used to safeguard the estimator against wrongly tracked features and influences of independently moving objects. We show the application of our approach in the real world by estimating the driven path of a vehicle in rough terrain from recorded stereo image sequences.