DynPL-SVO: A Robust Stereo Visual Odometry for Dynamic Scenes

Most feature-based stereo visual odometry (SVO) approaches estimate the motion of mobile robots by matching and tracking point features along a sequence of stereo images. However, in dynamic scenes mainly comprising moving pedestrians, vehicles, and so on, there are insufficient robust static point features to enable accurate motion estimation, causing failures when reconstructing robotic motion. In this article, we proposed DynPL-SVO, a complete dynamic SVO method that integrated united cost functions containing information between matched point features and re-projection errors perpendicular and parallel to the direction of the line features. Additionally, we introduced a dynamic grid algorithm to enhance its performance in dynamic scenes. The stereo camera motion was estimated through Levenberg–Marquard minimization of the re-projection errors of both point and line features. Comprehensive experimental results on KITTI and EuRoC MAV datasets showed that accuracy of the DynPL-SVO was improved by over 20% on average compared to other state-of-the-art SVO systems, especially in dynamic scenes.