论文标题
dynpl-svo：动态场景的强大立体视觉镜头
DynPL-SVO: A Robust Stereo Visual Odometry for Dynamic Scenes
论文作者
论文摘要
大多数基于功能的立体视觉探光计（SVO）通过沿一系列立体声图像匹配和跟踪点特征来估计移动机器人的运动。但是，在动态场景中，主要包括移动行人，车辆等，没有足够的稳健静态点特征来实现准确的运动估计，从而在重建机器人运动时会导致故障。在本文中，我们提出了Dynpl-Svo，这是一种完整的动态SVO方法，该方法集成了联合成本函数，该函数包含匹配点特征和垂直于垂直于线路方向的重新投影误差之间的信息。此外，我们引入了\ textit {dynamic} \ textit {grid}算法，以增强其在动态场景中的性能。通过Levenberg-Marquard最小化点和线特征的重新投影误差来估算立体声摄像机的运动。对Kitti和Euroc MAV数据集的全面实验结果表明，与其他最先进的SVO系统相比，Dynpl-SVO的准确性平均提高了20 \％，尤其是在动态场景中。
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.
