Fast Techniques for Monocular Visual Odometry

In this paper, fast techniques are proposed to achieve real time and robust monocular visual odometry. We apply an iterative 5-point method to estimate instantaneous camera motion parameters in the context of a RANSAC algorithm to cope with outliers efficiently. In our method, landmarks are localized in space using a probabilistic triangulation method utilized to enhance the estimation of the last camera pose. The enhancement is performed by multiple observations of landmarks and minimization of a cost function consisting of epipolar geometry constraints for far landmarks and projective constraints for close landmarks. The performance of the proposed method is demonstrated through application to the challenging KITTI visual odometry dataset.