论文标题
学会使用SLAM Outliers分割动态对象
Learning to Segment Dynamic Objects using SLAM Outliers
论文作者
论文摘要
我们提出了一种使用SLAM Outliers自动学习动态对象的方法。它仅需要一个每个动态对象的单眼序列才能训练，并在使用SLAM OUTLAIRS，创建掩码并使用这些掩码训练语义分割网络的情况下定位动态对象。我们将训练有素的网络集成到Orb-Slam 2和LDSO中。在运行时，我们删除动态对象上的功能，使大满贯不受它们的影响。我们还提出了一个新的立体声数据集和新的指标来评估SLAM鲁棒性。我们的数据集包括共识倒置，即大满贯在静态背景上使用更多功能的情况。共识倒置对SLAM来说是具有挑战性的，因为它们可能会导致重大的大满贯失败。我们的方法在单眼模式下以及单眼和立体声模式下的数据集中的TUM RGB-D数据集上的最新表现更好。
We present a method to automatically learn to segment dynamic objects using SLAM outliers. It requires only one monocular sequence per dynamic object for training and consists in localizing dynamic objects using SLAM outliers, creating their masks, and using these masks to train a semantic segmentation network. We integrate the trained network in ORB-SLAM 2 and LDSO. At runtime we remove features on dynamic objects, making the SLAM unaffected by them. We also propose a new stereo dataset and new metrics to evaluate SLAM robustness. Our dataset includes consensus inversions, i.e., situations where the SLAM uses more features on dynamic objects that on the static background. Consensus inversions are challenging for SLAM as they may cause major SLAM failures. Our approach performs better than the State-of-the-Art on the TUM RGB-D dataset in monocular mode and on our dataset in both monocular and stereo modes.
