Carnegie Mellon University
Direct Monocular Odometry Using Points and Lines

Shichao Yang and Sebastian Scherer
International Conference on Robotics and Automation (ICRA), June, 2017.

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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%

Associated Lab(s) / Group(s): Air Lab
Number of pages: 7

Text Reference
Shichao Yang and Sebastian Scherer, "Direct Monocular Odometry Using Points and Lines," International Conference on Robotics and Automation (ICRA), June, 2017.

BibTeX Reference
   author = "Shichao Yang and Sebastian Scherer",
   title = "Direct Monocular Odometry Using Points and Lines",
   booktitle = "International Conference on Robotics and Automation (ICRA)",
   month = "June",
   year = "2017",