ドローン空撮画像を用いた地表のセグメンテーションと密なオプティカルフローに基づく着陸可能領域の抽出
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| Published in | システム制御情報学会論文誌 Vol. 35; no. 5; pp. 109 - 117 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Japanese |
| Published |
一般社団法人 システム制御情報学会
15.05.2022
|
| Online Access | Get full text |
| ISSN | 1342-5668 2185-811X |
| DOI | 10.5687/iscie.35.109 |
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| Author | 菊本, 智寛 吉田, 武史 張本, 暘 浦久保, 孝光 |
|---|---|
| Author_xml | – sequence: 1 fullname: 菊本, 智寛 organization: 神戸大学 大学院 システム情報学研究科 – sequence: 1 fullname: 吉田, 武史 organization: 国立研究開発法人農業・食品産業技術総合研究機構農業ロボティクス研究センター – sequence: 1 fullname: 浦久保, 孝光 organization: 神戸大学 大学院 システム情報学研究科 – sequence: 1 fullname: 張本, 暘 organization: 神戸大学 大学院 システム情報学研究科 |
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| Copyright | 2022 一般社団法人 システム制御情報学会 |
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| EndPage | 117 |
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| References | [11] C. Kikumoto, Y. Harimoto, K. Isogaya, T. Yoshida and T. Urakubo: Landing site detection for UAVs based on CNNs classification and optical flow from monocular camera images; Journal of Robotics and Mechatronics, Vol. 33, No. 2 pp. 292–300 (2021) [3] X. Guo, S. Denman, C. Fookes, L. Mejias and S. Sridharan: Automatic UAV forced landing site detection using machine learning; Proc. of 2014 Int. Conf. on Digital Image Computing: Techniques and Applications, pp. 140–145 (2014) [7] S. Bosch, S. Lacroix and F. Caballero: Autonomous detection of safe landing areas for an UAV from monocular images; Proc. of 2006 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 5522–5527 (2006) [12] V. Badrinarayanan, A. Kendall and R. Cipolla: SegNet: A deep convolutional encoder-decoder architecture for image segmentation; IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 12, pp. 2481–2495 (2017) [1] K. Nonami: Drone technology, cutting-edge drone business and future prospects; Journal of Robotics and Mechatronics, Vol. 28, No. 3, pp. 262–272 (2016) [15] G. Farnebäck: Two-frame motion estimation based on polynomial expansion; Proc. of Scandinavian Conference on Image Analysis, pp. 363–370 (2003) [8] C. Forster, M. Faessler, F. Fontana, M. Werlberger and D. Scaramuzza: Continuous on-board monocular-vision-based elevation mapping applied to autonomous landing of micro aerial vehicles; Proc. of 2015 IEEE Int. Conf. on Robotics and Automation, pp. 111–118 (2015) [9] L. Yan, J. Qi, M. Wang, C. Wu and J. Xin: A safe landing site selection method of UAVs based on LiDAR point clouds; Proc. of 2020 39th Chinese Control Conference, pp. 6497–6502 (2020) [2] M. Warren, L. Mejias, X. Yang, B. Arain, F. Gonzalez and B. Upcroft: Enabling aircraft emergency landings using active visual site detection; Proc. of the 9th Conf. on Field and Service Robotics, pp. 1–14 (2013) [4] M. Laiacker, K. Kondak, M. Schwarzbach and T. Muskardin: Vision aided automatic landing system for fixed wing UAV; Proc. of 2013 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 2971–2976 (2013) [14] J. Shermeyer, T. Hossler, A. V. Etten, D. Hogan, R. Lewis and D. Kim: RarePlanes: Synthetic data takes flight; 2021 IEEE Winter Conference on Applications of Computer Vision, pp. 207–217 (2021) [10] T. Hinzmann, T. Stastny, C. Cadena, R. Siegwart and I. Gilitschenski: Free LSD: Prior-free visual landing site detection for autonomous planes; IEEE Robotics and Automation Letters, Vol. 3, No. 3, pp. 2545–2552 (2018) [5] X. Guo, S. Denman, C. Fookes and S. Sridharan: A robust UAV landing site detection system using mid-level discriminative patches; Proc. of the 23rd Int. Conf. on Pattern Recognition, pp. 1659–1664 (2016) [13] L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff and H. Adam: Encoder-decoder with atrous separable convolution for semantic image segmentation; European Conference on Computer Vision, arXiv:1802.02611 [cs.CV] (2018) [6] V. R. Desaraju, N. Michael, M. Humenberger, R. Brockers, S. Weiss, J. Nash and L. Matthies: Vision-based landing site evaluation and informed optimal trajectory generation toward autonomous rooftop landing; Autonomous Robots, Vol. 39, pp. 445–463 (2015) |
| References_xml | – reference: [8] C. Forster, M. Faessler, F. Fontana, M. Werlberger and D. Scaramuzza: Continuous on-board monocular-vision-based elevation mapping applied to autonomous landing of micro aerial vehicles; Proc. of 2015 IEEE Int. Conf. on Robotics and Automation, pp. 111–118 (2015) – reference: [1] K. Nonami: Drone technology, cutting-edge drone business and future prospects; Journal of Robotics and Mechatronics, Vol. 28, No. 3, pp. 262–272 (2016) – reference: [13] L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff and H. Adam: Encoder-decoder with atrous separable convolution for semantic image segmentation; European Conference on Computer Vision, arXiv:1802.02611 [cs.CV] (2018) – reference: [10] T. Hinzmann, T. Stastny, C. Cadena, R. Siegwart and I. Gilitschenski: Free LSD: Prior-free visual landing site detection for autonomous planes; IEEE Robotics and Automation Letters, Vol. 3, No. 3, pp. 2545–2552 (2018) – reference: [6] V. R. Desaraju, N. Michael, M. Humenberger, R. Brockers, S. Weiss, J. Nash and L. Matthies: Vision-based landing site evaluation and informed optimal trajectory generation toward autonomous rooftop landing; Autonomous Robots, Vol. 39, pp. 445–463 (2015) – reference: [3] X. Guo, S. Denman, C. Fookes, L. Mejias and S. Sridharan: Automatic UAV forced landing site detection using machine learning; Proc. of 2014 Int. Conf. on Digital Image Computing: Techniques and Applications, pp. 140–145 (2014) – reference: [11] C. Kikumoto, Y. Harimoto, K. Isogaya, T. Yoshida and T. Urakubo: Landing site detection for UAVs based on CNNs classification and optical flow from monocular camera images; Journal of Robotics and Mechatronics, Vol. 33, No. 2 pp. 292–300 (2021) – reference: [7] S. Bosch, S. Lacroix and F. Caballero: Autonomous detection of safe landing areas for an UAV from monocular images; Proc. of 2006 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 5522–5527 (2006) – reference: [4] M. Laiacker, K. Kondak, M. Schwarzbach and T. Muskardin: Vision aided automatic landing system for fixed wing UAV; Proc. of 2013 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 2971–2976 (2013) – reference: [12] V. Badrinarayanan, A. Kendall and R. Cipolla: SegNet: A deep convolutional encoder-decoder architecture for image segmentation; IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 12, pp. 2481–2495 (2017) – reference: [5] X. Guo, S. Denman, C. Fookes and S. Sridharan: A robust UAV landing site detection system using mid-level discriminative patches; Proc. of the 23rd Int. Conf. on Pattern Recognition, pp. 1659–1664 (2016) – reference: [9] L. Yan, J. Qi, M. Wang, C. Wu and J. Xin: A safe landing site selection method of UAVs based on LiDAR point clouds; Proc. of 2020 39th Chinese Control Conference, pp. 6497–6502 (2020) – reference: [15] G. Farnebäck: Two-frame motion estimation based on polynomial expansion; Proc. of Scandinavian Conference on Image Analysis, pp. 363–370 (2003) – reference: [2] M. Warren, L. Mejias, X. Yang, B. Arain, F. Gonzalez and B. Upcroft: Enabling aircraft emergency landings using active visual site detection; Proc. of the 9th Conf. on Field and Service Robotics, pp. 1–14 (2013) – reference: [14] J. Shermeyer, T. Hossler, A. V. Etten, D. Hogan, R. Lewis and D. Kim: RarePlanes: Synthetic data takes flight; 2021 IEEE Winter Conference on Applications of Computer Vision, pp. 207–217 (2021) |
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| Title | ドローン空撮画像を用いた地表のセグメンテーションと密なオプティカルフローに基づく着陸可能領域の抽出 |
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