DynPL-SVO：一种面向动态场景的鲁棒立体视觉里程计
DynPL-SVO: A Robust Stereo Visual Odometry for Dynamic Scenes
AI总结：
本文提出DynPL-SVO，一种结合点线特征联合代价函数与动态网格算法的立体视觉里程计方法，通过Levenberg-Marquardt最小化重投影误差估计运动，在KITTI和EuRoC MAV数据集上准确率平均提升超20%。
摘要：
大多数基于特征的立体视觉里程计（SVO）方法通过在立体图像序列中匹配和跟踪点特征来估计移动机器人的运动。然而，在主要由移动的行人、车辆等组成的动态场景中，缺乏足够鲁棒的静态点特征来实现准确的运动估计，导致在重建机器人运动时出现失败。在本文中，我们提出了DynPL-SVO，这是一种完整的动态SVO方法，它集成了联合代价函数，包含匹配点特征之间的信息以及垂直和平行于线特征方向的重投影误差。此外，我们引入了一种动态网格算法来增强其在动态场景中的性能。通过使用Levenberg-Marquardt最小化点特征和线特征的重投影误差来估计立体相机的运动。在KITTI和EuRoC MAV数据集上的综合实验结果表明，与其他最先进的SVO系统相比，DynPL-SVO的准确度平均提高了20%以上，尤其是在动态场景中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
