UAS Navigation in the Real World Using Visual Observation
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 12 references that are in the map. First posted on arXiv 25 Aug 2022.
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References · 6
- 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,443
- 2018Gibson Env: Real-World Perception for Embodied Agentscited by 481
- 2016Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learningcited by 366
- 2020Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learningcited by 14
- 2017One-Shot Reinforcement Learning for Robot Navigation with Interactive Replaycited by 4
- 2019Graph Attention Memory for Visual Navigationcited by 3
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