Self-Improving SLAM in Dynamic Environments: Learning When to Mask
This addresses the challenge of robust SLAM in dynamic settings for robotics and autonomous systems, representing an incremental improvement over existing masking approaches.
The paper tackles the problem of suboptimal object masking in visual SLAM for dynamic environments by proposing a method that learns when to mask objects to improve performance, achieving state-of-the-art results on TUM RGB-D and outperforming it on KITTI and a new ConsInv dataset.
Visual SLAM - Simultaneous Localization and Mapping - in dynamic environments typically relies on identifying and masking image features on moving objects to prevent them from negatively affecting performance. Current approaches are suboptimal: they either fail to mask objects when needed or, on the contrary, mask objects needlessly. Thus, we propose a novel SLAM that learns when masking objects improves its performance in dynamic scenarios. Given a method to segment objects and a SLAM, we give the latter the ability of Temporal Masking, i.e., to infer when certain classes of objects should be masked to maximize any given SLAM metric. We do not make any priors on motion: our method learns to mask moving objects by itself. To prevent high annotations costs, we created an automatic annotation method for self-supervised training. We constructed a new dataset, named ConsInv, which includes challenging real-world dynamic sequences respectively indoors and outdoors. Our method reaches the state of the art on the TUM RGB-D dataset and outperforms it on KITTI and ConsInv datasets.