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Efficient Drone Exploration in Real Unknown Environments

Published: 13 December 2022 Publication History

Abstract

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.

References

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Chen, K. -W., Xie, M. -R., Chen, Y. -M., Chu, T. -T. and Lin, Y. -B. 2022. DroneTalk : An Internet-of-Things-Based Drone System for Last-Mile Drone Delivery. In: IEEE Transactions on Intelligent Transportation Systems. 2022 ; Vol. 23, No. 9. pp. 15204.
[2]
Chu, T.-T., Chen, P.-H., Huang, P.-J. and Chen, K.-W. 2021. Collaborative Learning of Multiple-Discontinuous-Image Saliency Prediction for Drone Exploration. In Proceedings of the IEEE International Conference on Robotics and Automation, 2021.
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Dang, T., Papachristos, C. and Alexis, K. 2018. Visual Saliency-Aware Receding Horizon Autonomous Exploration with Application to Aerial Robotics. In Proceedings of the IEEE International Conference on Robotics and Automation, 2018.
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Huang, P. -J., Lu, C. -A. and Chen, K. -W. 2022. Temporally-Aggregating Multiple-Discontinuous-Image Saliency Prediction with Transformer-Based Attention. In Proceedings of the ICRA, 2022, pp. 6571-6577.
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Mur-Artal, R. and Tardós, J. D. 2017. An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras. In IEEE Transactions on Robotics, vol. 33, no. 5, pp. 1255-1262, Oct. 2017.
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Tan, Mingxing, and Le, Quoc. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. In Proceedings of the International conference on machine learning. PMLR, 2019.
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Zhang, K., Li, T., Shen, S., Liu, B., Chen, J. and Liu, Q. 2020. Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

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  • (2023)Three-Dimensional Drone Exploration with Saliency Prediction in Real Unknown EnvironmentsAerospace10.3390/aerospace1005048810:5(488)Online publication date: 22-May-2023

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        Published In

        cover image ACM Conferences
        SA '22: SIGGRAPH Asia 2022 Posters
        December 2022
        120 pages
        ISBN:9781450394628
        DOI:10.1145/3550082
        • Editors:
        • Soon Ki Jung,
        • Neil Dodgson
        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 13 December 2022

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        SA '22
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        SA '22: SIGGRAPH Asia 2022
        December 6 - 9, 2022
        Daegu, Republic of Korea

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        Overall Acceptance Rate 178 of 869 submissions, 20%

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        • (2023)Three-Dimensional Drone Exploration with Saliency Prediction in Real Unknown EnvironmentsAerospace10.3390/aerospace1005048810:5(488)Online publication date: 22-May-2023

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