| Title: | OS11-7 Development of a drone obstacle avoidance system based on depth estimation |
|---|---|
| Publication: | ICAROB2025 |
| Volume: | 30 |
| Pages: | 339-341 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2025.OS11-7 |
| Author(s): | Sora Takahashi, Eiji Hayashi |
| Publication Date: | February 13, 2025 |
| Keywords: | Deep Learning, Autonomous vehicle, Presumption of Depth |
| Abstract: | 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. |
| PDF File: | https://alife-robotics.co.jp/members2025/icarob/data/html/data/OS/OS11/OS11-7.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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