无人机与无人车协同导航，雪地环境定位精度达0.5米
Drone-Assisted UAV-UGV Collaboration for Autonomous Navigation in Snow-Covered Terrain
- 用轻量U-Net加合成雪景数据增强，实现96.5%道路分割准确率
- 融合GPS/IMU的卡尔曼滤波定位误差不超过±0.5米
- 基于视觉跟踪与动态路径规划，雪地遮蔽下仍可稳定导航
本文提出一种面向高海拔积雪地形的无人机-无人车协同导航框架，传统方法在低能见度与不稳地表下失效。设计一种满足实时性约束的定制轻量U-Net，结合创新合成雪景数据增强技术，实现96.5%的道路分割准确率。无人机定位采用融合机载GPS与惯性测量单元（IMU）数据的扩展卡尔曼滤波（EKF），最大位置误差为±0.5米。无人车通过无人机搭载的RGB-D相机提供的深度数据与YOLOv5视觉跟踪实现定位。动态路径规划算法基于分割结果实时调整以应对雪堆变化，在遮蔽测试环境中实现最小偏差成功导航。
原文摘要 · Abstract (English)
This paper presents a collaborative UAV-UGV navigation framework for high-altitude, snow-covered terrain, where reduced visibility and unstable ground render conventional methods ineffective. We introduce a custom efficient U-Net architecture that falls under the computational constraints for real-time road segmentation, utilizing a novel synthetic snow data augmentation technique to achieve 96.5% segmentation accuracy. For UAV localization, we implement an Extended Kalman Filter (EKF) fusing onboard GPS and IMU data, achieving a maximum observed positional error of +-0.5 meters. The UGV position is determined via a visual tracking pipeline using YOLOv5 and depth data from the UAV's RGB-D camera. A dynamic path planning algorithm utilizes this segmentation to adjust for snow drifts, enabling successful navigation in obscured test environment with minimal deviation.
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