论文标题
鲁棒映射的语义流引导运动删除方法
Semantic Flow-guided Motion Removal Method for Robust Mapping
论文作者
论文摘要
在场景中移动的物体对于大满贯系统仍然是一个严重的挑战。许多努力试图通过检测移动对象来删除图像中的运动区域。这样，在以后的计算中将忽略属于运动区域的关键点。在本文中，我们提出了一种新型的运动去除方法，利用语义信息和光流以提取运动区域。与以前的工作不同，我们不会直接从图像序列预测移动对象或运动区域。我们计算了由深度和姿势合成的刚性光流，并将其与估计的光流进行比较以获得初始运动区域。然后，我们利用k均值用实例分割掩模来填补运动区域掩模。与拟议的运动去除方法集成的ORB-SLAM2在室内和室外动态环境中都达到了最佳性能。
Moving objects in scenes are still a severe challenge for the SLAM system. Many efforts have tried to remove the motion regions in the images by detecting moving objects. In this way, the keypoints belonging to motion regions will be ignored in the later calculations. In this paper, we proposed a novel motion removal method, leveraging semantic information and optical flow to extract motion regions. Different from previous works, we don't predict moving objects or motion regions directly from image sequences. We computed rigid optical flow, synthesized by the depth and pose, and compared it against the estimated optical flow to obtain initial motion regions. Then, we utilized K-means to finetune the motion region masks with instance segmentation masks. The ORB-SLAM2 integrated with the proposed motion removal method achieved the best performance in both indoor and outdoor dynamic environments.
