Efficient Drone Exploration in Real Unknown Environments

Ming Ru Xie, Shing Yun Jung, Kuan Wen Chen

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

We propose an autonomous drone exploration system (ADES) with a lightweight and low-latency saliency prediction model to explore unknown environments. Recent studies have applied saliency prediction to drone exploration. However, these studies are not sufficiently mature. The ADES system proposes a smaller and faster saliency prediction model and adopts a novel drone exploration approach based on visual-inertial odometry (VIO) to solve the practical problems encountered during exploration, i.e., exploring salient objects without colliding with them and not repeatedly exploring salient objects. The system not only has a performance comparable to that of the state-of-the-art multiple-discontinuous-image saliency prediction network (TA-MSNet) but also enables drones to explore unknown environments more efficiently.

原文English
主出版物標題Proceedings - SIGGRAPH Asia 2022 Posters
編輯Stephen N. Spencer
發行者Association for Computing Machinery, Inc
ISBN(電子)9781450394628
DOIs
出版狀態Published - 26 12月 2022
事件SIGGRAPH Asia 2022 - Computer Graphics and Interactive Techniques Conference - Asia, SA 2022 - Daegu, 韓國
持續時間: 6 12月 20229 12月 2022

出版系列

名字Proceedings - SIGGRAPH Asia 2022 Posters

Conference

ConferenceSIGGRAPH Asia 2022 - Computer Graphics and Interactive Techniques Conference - Asia, SA 2022
國家/地區韓國
城市Daegu
期間6/12/229/12/22

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