A Method for Reconstructing Background from RGB-D SLAM in Indoor Dynamic Environments.

Dynamic environments are challenging for visual Simultaneous Localization and Mapping, as dynamic elements can disrupt the camera pose estimation and thus reduce the reconstructed map accuracy. To solve this problem, this study proposes an approach for eliminating dynamic elements and reconstructing static background in indoor dynamic environments. To check out dynamic elements, the geometric residual is exploited, and the static background is obtained after removing the dynamic elements and repairing images. The camera pose is estimated based on the static background. Keyframes are then selected using randomized ferns, and loop closure detection and relocalization are performed according to the keyframes set. Finally, the 3D scene is reconstructed. The proposed method is tested on the TUM and BONN datasets, and the map reconstruction accuracy is experimentally demonstrated.