Inspired by the recent success of application of dense data approach by using ORB-SLAM and RGB-D SLAM, we propose a better pipeline of real-time SLAM in dynamics environment. Different from previous SLAM which can only handle static scenes, we are presenting a solution which use RGB-D SLAM as well as YOLO real-time object detection to segment and remove dynamic scene and then construct static scene 3D. We gathered a dataset which allows us to jointly consider semantics, geometry, and physics and thus enables us to reconstruct the static scene while filtering out all dynamic objects.

翻译：在利用ORB-SLAM和RGB-D SLAM和RGB-D SLAM采用密集数据方法最近取得成功的启发下,我们提议在动态环境中建立更好的实时SLAM管道,不同于以往只能处理静态场景的SLAM,我们正在提出一种解决办法,即使用RGB-D SLAM和YOLO实时物体探测来分割和清除动态场景,然后建立静态场景3D。 我们收集了一个数据集,使我们能够共同考虑语义、几何和物理学,从而使我们能够在过滤所有动态物体的同时重建静态场景。</s>