A custom efficient U-Net architecture is introduced 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.
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.
Accurate velocity estimation in GPS-denied environments remains a core challenge for autonomous unmanned aerial vehicle (UAV) navigation. Our previous work demonstrated that fusing optical flow (OF) with dead reckoning (DR) substantially reduces position drift compared to inertial-only solutions. However, velocity esti...
Jakub Walczak, Piotr Targowski, S. Chmielewski et al.· Italian National Conference...· 0 citations
This work proposes a hybrid framework that integrates low-resolution Digital Elevation Model (DEM) data with real-time LiDAR-based obstacle detection and terrain analysis for efficient path planning and supports robust real-time adaptation, paving the way for more reliable autonomous navigation in dynamic outdoor envir...
Devender Singh, Issah N. Suleiman, Paul Mitten et al.· 0 citations
Autonomous navigation of multi-rotor Unmanned Aerial Vehicles (UAVs) in complex low-altitude environments remains challenging due to severe onboard computational constraints, sensor blind spots, and unpredictable dynamic obstacles. This paper presents a lightweight, integrated autonomous obstacle avoidance system for q...
Hong-Yu Fan, Yan-Li Wang, Wen-Xuan Chen· International Symposium on S...· 0 citations
The synthesis indicates that LiDAR remains a strong perceptual backbone for UAV autonomy, but that state-of-the-art systems increasingly combine it with inertial, visual, radar, and learning-based components rather than relying on LiDAR in isolation.
Svitlana Pavlova, V. Chepizhenko, Fu-Zhong Li et al.· Applied Sciences· 0 citations
Reliable UAV navigation is challenging when direct geometric perception is limited and only sparse environmental observations are available. This paper presents CoSafe-Nav, an integrated navigation framework that uses ultra-wideband (UWB) line-of-sight (LOS) observations to construct a candidate corridor and guide traj...
Visual SLAM (Simultaneous Localization and Mapping) is a core technology for UAV autonomous navigation. However, it suffers from cumulative errors and heading drift in texture-less environments (e.g., sky, water surfaces) or during longterm operation. Traditional methods relying on GPS or inertial sensors face limitati...
Junwei Lv, Qin-Bin Xu, Hao Liu et al.· International Conference on...· 0 citations
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