  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, etc., there are insufficient robust static point
features to enable accurate motion estimation, causing failures when
reconstructing robotic motion. In this paper, 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
\textit{dynamic} \textit{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.
