Onboard CNN-Based Processing for Target Detection and Autonomous Landing for MAVs
- 17 June 2020
- book chapter
- conference paper
- Published by Springer Science and Business Media LLC
- Vol. 12088, 195-208
- https://doi.org/10.1007/978-3-030-49076-8_19
Abstract
In this work, we address the problem of target detection involved in an autonomous landing task for a Micro Aerial Vehicle (MAV). The challenge is to detect a flag located somewhere in the environment. The flag is posed on a pole, and to its right, a landing platform is located. Thus, the MAV has to detect the flag, fly towards it and once it is close enough, locate the landing platform nearby, aiming at centring over it to perform landing; all of this has to be carried out autonomously. In this context, the main problem is the detection of both the flag and the landing platform, whose shapes are known in advanced. Traditional computer vision algorithms could be used; however, the main challenges in this task are the changes in illumination, rotation and scale, and the fact that the flight controller uses the detection to perform the autonomous flight; hence the detection has to be stable and continuous on every camera frame. Motivated by this, we propose to use a Convolutional Neural Network optimised to be run on a small computer with limited computer processing budget. The MAV carries this computer, and it is used to process everything on board. To validate our system, we tested with rotated images, changes in scale and the presence of low illumination. Our method is compared against two conventional computer vision methods, namely, template and feature matching. In addition, we tested our system performance in a wide corridor, executing everything on board the MAV. We achieved a successful detection of the flag with a confidence metric of 0.9386 and 0.9826 for the Landing platform. In total, all the onboard computations ran at an average of 13.01 fps.Keywords
This publication has 21 references indexed in Scilit:
- Recognition of Landing Target of UAV by Vision Using Machine LearningJournal of Signal Processing, 2019
- Autonomous Vehicular Landings on the Deck of an Unmanned Surface Vehicle using Deep Reinforcement LearningRobotica, 2019
- Vision-based UAV Guidance for Autonomous Landing with Deep Neural NetworksPublished by American Institute of Aeronautics and Astronautics (AIAA) ,2019
- Non-Rectangular RoI Extraction and Machine Learning Based Multiple Object Recognition Used for Time-Series Areal Images Obtained Using MAVProcedia Computer Science, 2018
- Toward End-to-End Control for UAV Autonomous Landing via Deep Reinforcement LearningPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- LightDenseYOLO: A Fast and Accurate Marker Tracker for Autonomous UAV Landing by Visible Light Camera Sensor on DroneSensors, 2018
- Autonomous navigation and landing of large jets using Artificial Neural Networks and learning by imitationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- A vision-based approach for autonomous landingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- Vision algorithms for fixed-wing unmanned aerial vehicle landing systemScience China Technological Sciences, 2017
- A controller for autonomous landing of AR.DronePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014