In autonomous navigation of mobile robots, sensors suffer from massive occlusion in cluttered environments, leaving significant amount of space unknown during planning. In practice, treating the unknown space in optimistic or pessimistic ways both set limitations on planning performance, thus aggressiveness and safety cannot be satisfied at the same time. However, humans can infer the exact shape of the obstacles from only partial observation and generate non-conservative trajectories that avoid possible collisions in occluded space. Mimicking human behavior, in this paper, we propose a method based on deep neural network to predict occupancy distribution of unknown space reliably. Specifically, the proposed method utilizes contextual information of environments and learns from prior knowledge to predict obstacle distributions in occluded space. We use unlabeled and no-ground-truth data to train our network and successfully apply it to real-time navigation in unseen environments without any refinement. Results show that our method leverages the performance of a kinodynamic planner by improving security with no reduction of speed in clustered environments.
翻译:在移动机器人的自主导航中,传感器在拥挤的环境中受到大规模封闭,在规划期间留下大量未知的空间。在实践中,以乐观或悲观的方式对待未知空间,对规划性能设置了限制,从而无法同时满足侵略性和安全性。然而,人类可以从局部观测中推断出障碍的确切形状,并产生避免隐蔽空间可能碰撞的非保守性轨道。在本文中,我们提出了一个基于深层神经网络的方法,以可靠地预测未知空间的占用分布。具体地说,拟议方法利用环境背景信息并从先前的知识中学习预测隐蔽空间的障碍分布。我们使用无标签和无地面图象数据来培训我们的网络,并在不作任何改进的情况下成功地将其应用于在隐蔽环境中的实时导航。结果显示,我们的方法通过在不降低聚居环境中的安全速度的情况下改进动态规划员的性能,从而利用了动态规划员的性能。