论文标题
在动态环境中进行自我改进的大满贯：学习何时掩盖
Self-Improving SLAM in Dynamic Environments: Learning When to Mask
论文作者
论文摘要
视觉大满贯 - 同时定位和映射 - 在动态环境中通常依赖于在移动对象上识别和掩盖图像特征，以防止它们对性能产生负面影响。当前方法是次优的：它们要么在需要时无法掩盖对象，要么相反，它们不必要地掩盖对象。因此，我们提出了一种新颖的猛击，在掩盖对象时会学会在动态场景中提高其性能。给定一种分割对象和猛击的方法，我们使后者具有时间掩盖的能力，即在应掩盖某些类别的对象时推断以最大程度地提高任何给定的SLAM度量。我们不会在运动上进行任何先验：我们的方法学会了自行掩盖移动对象。为了防止高注释成本，我们创建了一种自动注释方法，用于自我监督培训。我们构建了一个名为Consinv的新数据集，其中包括挑战现实世界动态序列在室内和室外。我们的方法在TUM RGB-D数据集上达到最新技术，并在KITTI和CONSINV数据集上胜过它。
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.
