论文标题
有效勘探和导航的占用预期
Occupancy Anticipation for Efficient Exploration and Navigation
论文作者
论文摘要
最先进的导航方法利用空间内存来推广到新环境，但是它们的占用图仅限于捕获代理直接观察到的几何结构。我们提出了预期的预期，代理商使用其以Egentric的RGB-D观测来推断可见区域以外的占用状态。在这样做的过程中，代理会更快地建立其空间意识，从而有助于在3D环境中有效探索和导航。通过在以自我为中心的观点和自上而下的地图中利用上下文，我们的模型成功地预期了环境的更广泛地图，其性能明显优于强大的基线。此外，当部署用于勘探和导航的顺序决策任务时，我们的模型在Gibson和Matterport3D数据集上的最先进方法优于最先进的方法。我们的方法是在2020年栖息地PointNav挑战赛中的获胜参赛作品。项目页面：http：//vision.cs.utexas.edu/projects/occupancy_anticipation/
State-of-the-art navigation methods leverage a spatial memory to generalize to new environments, but their occupancy maps are limited to capturing the geometric structures directly observed by the agent. We propose occupancy anticipation, where the agent uses its egocentric RGB-D observations to infer the occupancy state beyond the visible regions. In doing so, the agent builds its spatial awareness more rapidly, which facilitates efficient exploration and navigation in 3D environments. By exploiting context in both the egocentric views and top-down maps our model successfully anticipates a broader map of the environment, with performance significantly better than strong baselines. Furthermore, when deployed for the sequential decision-making tasks of exploration and navigation, our model outperforms state-of-the-art methods on the Gibson and Matterport3D datasets. Our approach is the winning entry in the 2020 Habitat PointNav Challenge. Project page: http://vision.cs.utexas.edu/projects/occupancy_anticipation/
