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Descent Angle Calculation for UAVs using Monocular Camera

by Dheeraj Komandur, Sagar Karki, Shebin Silvister, Gajendra Kashyap
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 31
Year of Publication: 2020
Authors: Dheeraj Komandur, Sagar Karki, Shebin Silvister, Gajendra Kashyap
10.5120/ijca2020920860

Dheeraj Komandur, Sagar Karki, Shebin Silvister, Gajendra Kashyap . Descent Angle Calculation for UAVs using Monocular Camera. International Journal of Computer Applications. 175, 31 ( Nov 2020), 28-33. DOI=10.5120/ijca2020920860

@article{ 10.5120/ijca2020920860,
author = { Dheeraj Komandur, Sagar Karki, Shebin Silvister, Gajendra Kashyap },
title = { Descent Angle Calculation for UAVs using Monocular Camera },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2020 },
volume = { 175 },
number = { 31 },
month = { Nov },
year = { 2020 },
issn = { 0975-8887 },
pages = { 28-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number31/31650-2020920860/ },
doi = { 10.5120/ijca2020920860 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:39:59.749717+05:30
%A Dheeraj Komandur
%A Sagar Karki
%A Shebin Silvister
%A Gajendra Kashyap
%T Descent Angle Calculation for UAVs using Monocular Camera
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 31
%P 28-33
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, an approach for solid angle calculation for autonomous landing of UAVs/AVs using a monocular camera and 2D image of the helipad is introduced. Autonomous landing has always been a prime research field that is complex and unique in the field of automation. The proposed method that uses object detection and homography to generate relative flight angles - Yaw, Pitch, and Roll. First, an object detection algorithm is used to locate the landing pad in an image and extract the required region of interest (ROI). This ROI is then preprocessed using OpenCV and key corners of the landing pad are extracted. Further, these extracted coordinates are mapped to a reference image and relative homography is calculated. Decomposing the homography matrix results in Euler angles which give the angles of UAVs/AVs with respect to the landing pad.

References
  1. O. A. YakimenkoiI. I. Kaminerw. J. Lentzp. A. Ghyzel, “Unmanned Aircraft Navigation For Shipboard Landing UsingInfrared Vision”, August  2002.
  2. G.Anitha, Dr. R.N  Gireesh Kumar, “Vision Based Autonomous Landing of an Unmanned Aerial Vehicle”, September 2012.
  3. J. Janousek and P. Marcon, "Precision landing options in unmanned aerial vehicles," 2018 International Interdisciplinary PhD Workshop (IIPhDW), Swinoujście, 2018, pp. 58-60, doi: 10.1109/IIPHDW.2018.8388325.
  4. Sanchez-Lopez JL, Saripalli S, Campoy P, et al. Toward visual autonomous ship board landing of a VTOL UAV. In: International conference on unmanned aircraft systems (ICUAS), Atlanta, GA, USA, 28–31 May 2013.
  5. Premachandra C, Yoshida T and Kato K. A basic study of landing system for multicopters using Raspberry Pi. In: International symposium on consumer electronics (ISCE), Madrid, Spain, 24–26 June 2015.
  6. Lin S, Garrat MA and Lambert AJ. Monocular vision-based real-time target recognition and tracking for autonomously landing an UAV in a cluttered shipboard environment. Auton Robot 2017; 41(4): 881–901.  DOI: 10.1007/s10514-016-9564-2.
  7. Oliveira, César S. de, A. Anvar, Marília Costa Rosendo Silva, Armando Alves Neto and Leonardo Amaral Mozelli. “COMPARISON OF CASCADE CLASSIFIERS FOR AUTOMATIC LANDING PAD DETECTION IN DIGITAL IMAGES.” (2015).
  8. Arora, Sankalp, Sezal Jain, Sebastian A. Scherer, Stephen Nuske, Lyle Chamberlain and Sanjiv Singh. “Infrastructure-free shipdeck tracking for autonomous landing.” 2013 IEEE International Conference on Robotics and Automation (2013): 323-330.
  9. Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection”, 23 Apr 2020.
  10. Homography - an overview | ScienceDirect Topics. https://www.sciencedirect.com/topics/engineering/homography
  11. A. Buades, B. Coll and J. M. Morel, "Denoising image sequences does not require motion estimation," IEEE Conference on Advanced Video and Signal Based Surveillance, 2005.
  12. OpenCV Reference Manual. https://docs.opencv.org/
  13. Satoshi Suzuki and others. Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing, 30(1):32–46, 1985.
  14. ‘Ramer–Douglas–Peucker algorithm’ (2020) Wikipedia. Available at: http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm
  15. Zhang Z. (2014) Camera Parameters (Intrinsic, Extrinsic). In: Ikeuchi K. (eds) Computer Vision. Springer, Boston, MA.
  16. Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg, “SSD: Single Shot MultiBox Detector”, 2016.
  17. Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick, “Mask R-CNN”, 24 Jan 2018.
  18. Tan, Mingxing & Pang, Ruoming & Le, Quoc. (2019). EfficientDet: Scalable and Efficient Object Detection.
  19. TensorFlow Object Detection API is a Machine Learning modeling library consisting of various pre-trained models. https://github.com/tensorflow/models/tree/master/research/object_detection”
Index Terms

Computer Science
Information Sciences

Keywords

Autonomous Landing UAVs

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