StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments

In this paper we propose a method for robust dense RGB-D SLAM in dynamic environments which detects moving objects and simultaneously reconstructs the background structure. Dynamic environments are challenging for visual SLAM as moving objects can impair camera pose tracking and cause corruptions to be integrated into the map. While most methods employ implicit robust penalizers or outlier filtering techniques in order to handle moving objects, our approach is to simultaneously estimate the camera motion as well as a probabilistic static/dynamic segmentation of the current RGBD image pair. This segmentation is then used for weighted dense RGB-D fusion to estimate a 3D model of only the static parts of the environment. By leveraging the 3D model for frameto-model alignment, as well as static/dynamic segmentation, camera motion estimation has reduced overall drift — as well as being more robust to the presence of dynamics in the scene. Demonstrations are presented which compare the proposed method to comparable state-of-the-art approaches using both static and dynamic sequences. The proposed method achieves similar performance in static environments and improved accuracy and robustness in dynamic scenes.