论文标题
UAS在现实世界中使用视觉观察
UAS Navigation in the Real World Using Visual Observation
论文作者
论文摘要
本文介绍了一种新颖的端到端无人空中系统（UAS）导航方法，用于现实世界中的远程视觉导航。受到人类本能的双过程视觉导航系统的启发：环境理解和地标识别，我们将UAS导航任务分为两个相同的阶段。我们的系统结合了增强学习（RL）和图像匹配方法。首先，代理在指定环境中使用RL学习导航策略。为了实现这一目标，我们为培训过程设计了互动的UASNAV环境。一旦代理商学习了导航政策，这意味着“熟悉环境”，我们就让UAS在现实世界中飞行，以使用图像匹配方法识别地标，并根据知识渊博的政策采取行动。在导航过程中，UAS嵌入单个相机作为唯一的视觉传感器。我们证明，UAS可以学习在现实世界中最短的路径距离起点几百米之遥的目的地。
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.
