Lidar-Monocular Visual Odometry using Point and Line Features

Abstract

We introduce a novel lidar-monocular visual odometry approach using point and line features. Compared to previous point-only based lidar-visual odometry, our approach leverages more environment structure information by introducing both point and line features into pose estimation. We provide a robust method for point and line depth extraction, and formulate the extracted depth as prior factors for point-line bundle adjustment. This method greatly reduces the features' 3D ambiguity and thus improves the pose estimation accuracy. Besides, we also provide a purely visual motion tracking method and a novel scale correction scheme, leading to an efficient lidar-monocular visual odometry system with high accuracy. The evaluations on the public KITTI odometry benchmark show that our technique achieves more accurate pose estimation than the state-of-the-art approaches, and is sometimes even better than those leveraging semantic information.