无人机辅助的无人机-无人车协同积雪覆盖地形自主导航
Drone-Assisted UAV-UGV Collaboration for Autonomous Navigation in Snow-Covered Terrain
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中文总结 AI 辅助
针对积雪覆盖地形传统导航方法失效的问题，提出无人机-无人车协同导航框架，采用定制U-Net、EKF、YOLOv5等技术实现高分割精度与低定位误差，可在遮挡环境中完成自主导航。
中文摘要 AI 辅助
本文提出一种适用于高海拔积雪覆盖地形的无人机-无人车协同导航框架，该地形中能见度降低与地面不稳定导致传统方法失效。我们引入定制高效U-Net架构，其满足实时道路分割的计算约束，采用新型合成积雪数据增强技术实现96.5%的分割精度。对于无人机定位，我们实现融合机载GPS与IMU数据的扩展卡尔曼滤波（EKF），观测到的最大位置误差为±0.5米。无人车位置通过使用YOLOv5的视觉跟踪管道与无人机RGB-D相机的深度数据确定。动态路径规划算法利用该分割结果调整以应对雪堆，在存在遮挡的测试环境中实现偏差极小的成功导航。
英文摘要
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.
