Improving accuracy for Ego vehicle motion estimation using epipolar geometry

This paper presents an original method for increasing the accuracy of ego vehicle motion estimation using video data. Our algorithm takes as input a monocular video sequence on which originally combines procedures for feature detection and filtering, optical flow, epipolar geometry and estimation of the rotation from the obtained essential matrix. Imposing a movement constraint on the rotation matrix, we obtain a powerful method for estimating the rotation of the vehicle from frame to frame. Furthermore, the obtained rotation and stereo data are used for computing the translation of the vehicle. The use of stereo data only for translation estimation diminishes the influence of stereo errors on rotation matrix. Experiments have been performed using various urban traffic scenes, with horizontal and vertical curvatures revealing a high degree of accuracy compared to reference measurements.