Development of a drone obstacle avoidance system based on depth estimation

This study developed an obstacle avoidance system for drones using depth estimation from RGB cameras, aiming to reduce reliance on expensive sensors like RGB-D cameras or LiDAR.The system employs the deep learning model ZoeDepth for depth estimation and integrates it with ROS and Gazebo for simulation.Two autonomous systems were evaluated: one using RGB-D cameras and the other using depth estimation with RGB cameras.Experimental results show that while the RGB-D camera system outperformed in accuracy, the depth estimation-based system provided cost-effective and reasonable performance, especially in complex environments.The research concludes with plans to improve the system for denser obstacle environments and conduct real-world experiments.