用单目深度估计实现低成本智能越野车，真实场景更稳定
Depth-Aware Rover: A Study of Edge AI and Monocular Vision for Real-World Implementation
- 用单目深度模型替代双目摄像头，降低硬件成本
- 实测达到0.1帧/秒深度输出、10帧/秒目标检测速度
- 适合资源受限的边缘设备部署，尤其适用于火星探测等任务
本研究分析了深度感知机器人在模拟与真实环境中的导航实现，重点对比了双目视觉与单目深度估计在边缘AI下的表现。使用基于Unity的月面地形模拟器配合立体相机与OpenCV的StereoSGBM生成视差图；实际搭建的树莓派4平台则采用UniDepthV2进行单目度量深度估计，结合YOLO12n实现实时目标检测。仿真中双目方案精度更高，但在真实部署中，单目方法展现出更强鲁棒性与更低的成本，实测获得0.1 FPS的深度输出和10 FPS的目标检测速度。
原文摘要 · Abstract (English)
This study analyses simulated and real-world implementations of depth-aware rover navigation, highlighting the transition from stereo vision to monocular depth estimation using edge AI. A Unity-based lunar terrain simulator with stereo cameras and OpenCV's StereoSGBM was used to generate disparity maps. A physical rover built on Raspberry Pi 4 employed UniDepthV2 for monocular metric depth estimation and YOLO12n for real-time object detection. While stereo vision yielded higher accuracy in simulation, the monocular approach proved more robust and cost-effective in real-world deployment, achieving 0.1 FPS for depth and 10 FPS for detection.
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