Automatic Pose Estimation of Micro Unmanned Aerial Vehicle for Autonomous Landing. Shrestha, M., Panday, S. P., Joshi, B., Shakya, A., & Pandey, R. K. In Huang, D. & Premaratne, P., editors, Intelligent Computing Methodologies: Proceedings of the16th International Conference on Intelligent Computing, ICIC 2020, volume 12465, of Lecture Notes in Computer Science, pages 3–15, Switzerland, October, 2020. Springer Nature. ![Automatic Pose Estimation of Micro Unmanned Aerial Vehicle for Autonomous Landing [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 1 download The guided navigation has enabled users with minimal amount of training to navigate and perform flight mission of micro unmanned aerial vehicle (MAV). In non-urban areas, where there are no other aerial traffic and congestion, MAV take-off & travel does not need much Global Positioning System (GPS) accuracy. The critical part seems to be during the landing of the MAV, where slight GPS inaccuracy can lead to landing of the vehicle in the dangerous spot, causing damage to the MAV. This paper aims to propose a low cost portable solution for the Autonomous landing of the MAV, using object detection and machine learning techniques. In this work, You Only Look Once (YOLO) has been used for object detection and corner detection algorithm along with projective transformation equation has been used for getting the position of MAV with respect to the landing spot has been devised. The experiments were carried with Raspberry Pi and the estimation shows up to 4% of error in height and 12.5% error in X, Y position.
@inproceedings{shrestha2020automatic,
abstract = {The guided navigation has enabled users with minimal amount of training to navigate and perform flight mission of micro unmanned aerial vehicle (MAV). In non-urban areas, where there are no other aerial traffic and congestion, MAV take-off & travel does not need much Global Positioning System (GPS) accuracy. The critical part seems to be during the landing of the MAV, where slight GPS inaccuracy can lead to landing of the vehicle in the dangerous spot, causing damage to the MAV. This paper aims to propose a low cost portable solution for the Autonomous landing of the MAV, using object detection and machine learning techniques. In this work, You Only Look Once (YOLO) has been used for object detection and corner detection algorithm along with projective transformation equation has been used for getting the position of MAV with respect to the landing spot has been devised. The experiments were carried with Raspberry Pi and the estimation shows up to 4% of error in height and 12.5% error in X, Y position.},
added-at = {2023-03-07T12:13:40.000+0100},
address = {Switzerland},
author = {Shrestha, Manish and Panday, Sanjeeb Prasad and Joshi, Basanta and Shakya, Aman and Pandey, Rom Kant},
biburl = {https://www.bibsonomy.org/bibtex/2622539c970d1cc076e98aed0abfee5f4/amanshakya},
booktitle = {Intelligent Computing Methodologies: Proceedings of the16th International Conference on Intelligent Computing, ICIC 2020},
doi = {https://doi.org/10.1007/978-3-030-60796-8_1},
editor = {Huang, De-Shuang and Premaratne, Prashan},
eventdate = {October 2–5, 2020},
eventtitle = {16th International Conference on Intelligent Computing, ICIC 2020},
interhash = {0e0cc0c7323f2423f47bdd1c4422c8a2},
intrahash = {622539c970d1cc076e98aed0abfee5f4},
keywords = {imported myown ugc},
month = {October},
pages = {3--15},
publisher = {Springer Nature},
series = {Lecture Notes in Computer Science},
timestamp = {2023-03-10T07:47:26.000+0100},
title = {Automatic Pose Estimation of Micro Unmanned Aerial Vehicle for Autonomous Landing},
url = {https://doi.org/10.1007/978-3-030-60796-8_1},
venue = {Bari, Italy},
volume = 12465,
year = 2020
}
Downloads: 1
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