DynPL-SVO: A New Method Using Point and Line Features for Stereo Visual Odometry in Dynamic Scenes

Stereo visual odometry is widely used where a robot tracks its position and orientation using stereo cameras. Most of the approaches recovered mobile robotics motion based on the matching and tracking of point features along a sequence of stereo images. But in low-textured and dynamic scenes, there are no sufficient robust static point features for motion estimation, causing lots of previous work to fail to reconstruct the robotic motion. However, line features can be detected in such low-textured and dynamic scenes. In this paper, we proposed DynPL-SVO, a stereo visual odometry with the $dynamic$ $grid$ algorithm and the cost function containing both vertical and horizontal information of the line features. Stereo camera motion was obtained through Levenberg-Marquard minimization of re-projection error of point and line features. The experimental results on the KITTI and EuRoC MAV datasets showed that the DynPL-SVO had a competitive performance when compared to other state-of-the-art systems by producing more robust and accurate motion estimation, especially in low-textured and dynamic scenes.