Autonomic drone landing system based on LEDs pattern and visual markers recognition

The idea of an autonomic drone landing system based on markers recognition and LEDs (Light-emitting diode) detection is presented in this paper. A safe landing process is one of the most important parts connected with drone mission. The proposed method consists of two main parts: marker recognition and LEDs detection. The issue of marker recognition was widely described in our previous research, in this paper, however, we present the idea of detecting LEDs placed near the marker to improve the detection process. The main problem connected with this issue is creation of the best possible LEDs detection algorithm which would make the detection process accurate and robust. Four different algorithms, that were created by the authors are presented and compared in order to select the best one. We also present the idea of the whole landing system based on both markers recognition and LEDs detection algorithms connected in the way that they make reliable and accurate solution.

[1]  Marcin Skoczylas,et al.  Multirotor micro air vehicle autonomous landing system based on image markers recognition , 2017, Symposium on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (WILGA).

[2]  Hong Jiang,et al.  Landing system for AR.Drone 2.0 using onboard camera and ROS , 2016, 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC).

[3]  Md. Zainal Abedin,et al.  Traffic Sign Recognition using SURF: Speeded up robust feature descriptor and artificial neural network classifier , 2016, 2016 9th International Conference on Electrical and Computer Engineering (ICECE).

[4]  Dhanashri Mulmule-Shirkhedkar,et al.  Comparative study of SURF and FREAK descriptor on Indian Rupee Currency notes , 2015, 2015 International Conference on Information Processing (ICIP).

[5]  Pierre Vandergheynst,et al.  FREAK: Fast Retina Keypoint , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Bo Wang,et al.  Robust traffic sign recognition and tracking for Advanced Driver Assistance Systems , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[7]  Tianjiang Hu,et al.  BRISK based target localization for fixed-wing UAV's vision-based autonomous landing , 2015, 2015 IEEE International Conference on Information and Automation.

[8]  Zdzisław Gosiewski,et al.  Modelling of Dynamic and Control of Six-Rotor Autonomous Unmanned Aerial Vehicle , 2013 .

[9]  Ruhina Karani,et al.  Detection of landing areas for unmanned aerial vehicles , 2015, 2016 International Conference on Computing Communication Control and automation (ICCUBEA).

[10]  Fabrício Martins Lopes,et al.  Combining SURF descriptor and complex networks for face recognition , 2016, 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).

[11]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.

[12]  Ashok Kumar Bathla,et al.  Video stabilization for an aerial surveillance system using sift and surf , 2016, 2016 2nd International Conference on Next Generation Computing Technologies (NGCT).

[13]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[14]  S. G. Shikalpure,et al.  Image re-ranking using sift feature , 2016, 2016 International Conference on Communication and Electronics Systems (ICCES).

[15]  Gary Bradski,et al.  Computer Vision Face Tracking For Use in a Perceptual User Interface , 1998 .

[16]  Marcus Liwicki,et al.  FREAK for Real Time Forensic Signature Verification , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[17]  Tzuu-Hseng S. Li,et al.  Implementation of real-time object recognition system for home-service robot by integrating SURF and BRISK , 2014, 2014 IEEE International Conference on System Science and Engineering (ICSSE).

[18]  Roman Bartak,et al.  A controller for autonomous landing of AR.Drone , 2014, The 26th Chinese Control and Decision Conference (2014 CCDC).

[19]  Daniel Oldziej,et al.  Ground control station software design for micro aerial vehicles , 2017, Symposium on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (WILGA).

[20]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..