This paper presents a novel end-to-end Unmanned Aerial System (UAS) navigation approach for long-range visual navigation in the real world. Inspired by dual-process visual navigation system of human's instinct: environment understanding and landmark recognition, we formulate the UAS navigation task into two same phases. Our system combines the reinforcement learning (RL) and image matching approaches. First, the agent learns the navigation policy using RL in the specified environment. To achieve this, we design an interactive UASNAV environment for the training process. Once the agent learns the navigation policy, which means 'familiarized themselves with the environment', we let the UAS fly in the real world to recognize the landmarks using image matching method and take action according to the learned policy. During the navigation process, the UAS is embedded with single camera as the only visual sensor. We demonstrate that the UAS can learn navigating to the destination hundreds meters away from the starting point with the shortest path in the real world scenario.
翻译:本文介绍了在现实世界中远程视觉导航的新型端到端无人驾驶航空系统(UAS)导航方法。在人类本能的两过程视觉导航系统的启发下,我们将UAS导航任务分为两个相同的阶段。我们的系统结合了强化学习(RL)和图像匹配方法。首先,代理人在特定环境中学习使用RL的导航政策。为了实现这一点,我们设计了一个互动的UASNAV环境用于培训过程。一旦代理人学习了导航政策,即“与环境相适应”,我们就让UAS在现实世界中使用图像匹配方法识别这些标志,并根据所学的政策采取行动。在导航过程中,UAS以单一的相机嵌入为唯一的视觉传感器。我们证明UAS可以学习从起始点到目的地数百米处的导航,从现实世界情景中最短的路径到最短路径。