论文标题
流汇：基于光流的动态密集RGB-D大满贯
FlowFusion: Dynamic Dense RGB-D SLAM Based on Optical Flow
论文作者
论文摘要
动态环境对于视觉量很大，因为移动对象会遮住静态环境特征并导致错误的相机运动估计。在本文中，我们提出了一种新型的致密RGB-D SLAM解决方案，该解决方案同时完成了动态/静态分割和相机自我移动估计以及静态背景重建。我们的新颖性是使用光流残差来突出RGB-D点云中的动态语义，并为相机跟踪和背景重建提供了更准确，更有效的动态/静态分段。公共数据集和真实动态场景上的密集重建结果表明，与最先进的方法相比，所提出的方法在动态环境和静态环境中都可以实现准确，有效的性能。
Dynamic environments are challenging for visual SLAM since the moving objects occlude the static environment features and lead to wrong camera motion estimation. In this paper, we present a novel dense RGB-D SLAM solution that simultaneously accomplishes the dynamic/static segmentation and camera ego-motion estimation as well as the static background reconstructions. Our novelty is using optical flow residuals to highlight the dynamic semantics in the RGB-D point clouds and provide more accurate and efficient dynamic/static segmentation for camera tracking and background reconstruction. The dense reconstruction results on public datasets and real dynamic scenes indicate that the proposed approach achieved accurate and efficient performances in both dynamic and static environments compared to state-of-the-art approaches.
