Drone-Assisted UAV-UGV Collaboration for Autonomous Navigation in Snow-Covered Terrain

2026-08-07

AI Summary

This study addresses the challenges of autonomous navigation in high-altitude snowy environments, where low visibility and unstable terrain often render conventional methods ineffective. To overcome these limitations, the authors propose a collaborative navigation framework integrating unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). The approach combines a lightweight U-Net for real-time road segmentation, an enhanced synthetic snow data augmentation strategy, YOLOv5-based visual tracking of the UGV using RGB-D depth information, and high-precision UAV localization via an extended Kalman filter fusing GPS and IMU data, all coordinated through dynamic path planning for adaptive environmental navigation. Experimental results demonstrate a road segmentation accuracy of 96.5% and UAV localization errors within ±0.5 meters, achieving robust, high-precision, low-drift autonomous cooperative navigation under low-visibility snowy conditions.

Abstract

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.

Problem

• UAV-UGV collaboration
• autonomous navigation
• snow-covered terrain
• road segmentation
• visual tracking

Innovation

• UAV-UGV collaboration
• synthetic snow data augmentation
• real-time road segmentation
• Extended Kalman Filter
• dynamic path planning