@article{M44014EA2,
title = "Semi-Permeable Obstacle Recognition and Avoidance System for Autonomous UAV Using Stereo Camera",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2024",
issn = "1226-4717",
doi = "10.7840/kics.2024.49.3.401",
author = "Dae Hyeon Kwon, Soo Young Shin",
keywords = "UAV, Collision Avoidance, Optimal path, Deep learning, Image processing, Wire fence",
abstract = "This paper proposes a system for autonomous UAVs to detect and navigate around semi-permeable obstacles, such as a wire fence. The proposed method employs a stereo camera to detect semi-permeable obstacles using a Convolutional Neural Network (CNN) based object detection algorithm and utilizes image processing techniques such as Canny Edge Detection to eliminate the background of obstacles. This enables precise decision of the three-dimensional position of obstacles through the utilization of the collected depth information. Additionally, the system incorporates the Fast-Planner, which is a path-planning algorithm, to map the semi-permeable obstacles and create avoidance trajectories. The experimental results validate the proposed method improves the precision of the obstacle location compared to conventional 3D object detection. In addition, it could be effective to generate the obstacle avoidance routes by the path planning algorithm."
}