Direct Monocular Odometry Using Points and Lines
This work addresses robustness issues in visual odometry for robotics and autonomous systems, offering an incremental improvement by combining existing methods.
The paper tackles the problem of monocular visual odometry by incorporating both points and edges to improve robustness in texture-less environments and under lighting changes, achieving over 50% error reduction in some challenging cases.
Most visual odometry algorithm for a monocular camera focuses on points, either by feature matching, or direct alignment of pixel intensity, while ignoring a common but important geometry entity: edges. In this paper, we propose an odometry algorithm that combines points and edges to benefit from the advantages of both direct and feature based methods. It works better in texture-less environments and is also more robust to lighting changes and fast motion by increasing the convergence basin. We maintain a depth map for the keyframe then in the tracking part, the camera pose is recovered by minimizing both the photometric error and geometric error to the matched edge in a probabilistic framework. In the mapping part, edge is used to speed up and increase stereo matching accuracy. On various public datasets, our algorithm achieves better or comparable performance than state-of-the-art monocular odometry methods. In some challenging texture-less environments, our algorithm reduces the state estimation error over 50%.