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

arXiv: https://arxiv.org/abs/2608.07797 · HTML: https://mlsanity.com/paper/2608.07797/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 6 of 6)

- [IDD-AW: A Benchmark for Safe and Robust Segmentation of Drive Scenes in Unstructured Traffic and Adverse Weather](https://mlsanity.com/paper/2311.14459/) (2023)
- [Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned](https://mlsanity.com/paper/2111.13981/) (2021)
- [Online Motion Planning based on Nonlinear Model Predictive Control with Non-Euclidean Rotation Groups](https://mlsanity.com/paper/2006.03534/) (2020)
- [Applying Unsupervised Semantic Segmentation to High-Resolution UAV Imagery for Enhanced Road Scene Parsing](https://mlsanity.com/paper/2402.02985/) (2024)
- [Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images using SegFormer](https://mlsanity.com/paper/2410.01092/) (2024)
- [Adaptive Semantic Communication for UAV/UGV Cooperative Path Planning](https://mlsanity.com/paper/2510.06901/) (2025)

## Abstract (arXiv)

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.