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.

本文提出了一种新型 SLAM 方法，该方法能够学习到在动态环境中通过遮盖动态物体的方式来提高其性能；我们构建了一个名为 ConsInv 的新数据集，并证明了我们的方法在 TUM RGB-D 数据集上达到了现有水平，并在 KITTI 和 ConsInv 数据集上表现优异。