论文标题
点：视觉大满贯的动态对象跟踪
DOT: Dynamic Object Tracking for Visual SLAM
论文作者
论文摘要
在本文中，我们介绍了DOT（动态对象跟踪），在现有的SLAM系统中添加的前端可以显着提高其在高度动态环境中的鲁棒性和准确性。 DOT结合了实例细分和多视图几何形状，以生成动态对象的掩码，以便允许基于刚性场景模型的SLAM系统以避免其优化中的此类图像区域。 为了确定哪些对象实际移动，点段首先是潜在动态对象的实例，然后，通过估计的摄像头运动，通过最小化光度重投影误差来跟踪此类对象。这种短期跟踪提高了相对于其他方法的分割的准确性。最后，仅生成实际动态掩码。我们已经在三个公共数据集中使用Orb-Slam 2评估了DOT。我们的结果表明，我们的方法可显着提高Orb-Slam 2的准确性和鲁棒性，尤其是在高度动态的场景中。
In this paper we present DOT (Dynamic Object Tracking), a front-end that added to existing SLAM systems can significantly improve their robustness and accuracy in highly dynamic environments. DOT combines instance segmentation and multi-view geometry to generate masks for dynamic objects in order to allow SLAM systems based on rigid scene models to avoid such image areas in their optimizations. To determine which objects are actually moving, DOT segments first instances of potentially dynamic objects and then, with the estimated camera motion, tracks such objects by minimizing the photometric reprojection error. This short-term tracking improves the accuracy of the segmentation with respect to other approaches. In the end, only actually dynamic masks are generated. We have evaluated DOT with ORB-SLAM 2 in three public datasets. Our results show that our approach improves significantly the accuracy and robustness of ORB-SLAM 2, especially in highly dynamic scenes.
