用多架无人机跨时段数据融合，实现无GPS环境下的高精度定位
Cross-Session 3D LiDAR and Camera Fusion for Robust Localization of Unmanned Aerial Vehicles in GPS-Denied Environments
- 跨时段融合多架无人机采集的视觉与激光点云数据
- 在特征稀疏场景下定位误差接近有GPS时的表现
- 仅需单目相机+激光雷达，无需复杂传感器配置
无人飞行器（UAV）在结构健康监测等应用中，精准定位至关重要，尤其在室内、隧道、城市峡谷或大型建筑下方等全球定位系统（GPS）信号不可靠或被屏蔽的环境中。为应对这一挑战，本文提出一种名为Cross-Fusion的新方法，可在实时条件下实现无人机定位。该方法融合3D激光雷达（LiDAR）与单目相机数据，关键创新在于跨时段融合策略：利用多架无人机在常规基线勘测中收集的视觉与几何信息，提升定位一致性与地图完整性。系统采用激光雷达里程计进行运动追踪，并通过单色RGB相机进行图像特征匹配以纠正漂移、提高精度。与视觉惯性系统相比，Cross-Fusion保持简单传感器配置，避免了立体或多帧同步等复杂设计。实验表明，该方法在特征稀疏环境下仍能实现与基于GPS方法相当的定位精度，且运行稳定可靠。
原文摘要 · Abstract (English)
Accurate localization of unmanned aerial vehicles (UAVs) is essential for applications such as structural health monitoring, especially in environments where Global Positioning System (GPS) signals are denied or unreliable, like indoor spaces, tunnels, urban canyons, or areas beneath large structures. To address this challenge, we propose Cross-Fusion, a novel method for real-time UAV localization that integrates data from a 3D Light Detection and Ranging (LiDAR) and a monocular camera. A key contribution is its cross-session fusion strategy, which integrates visual and geometric information collected from multiple agents during routine baseline surveys to improve localization consistency and map completeness. The system employs LiDAR-based odometry for motion tracking and image-based feature matching via a single red-green-blue (RGB) camera to correct drift and improve accuracy. Unlike visual-inertial systems, Cross-Fusion maintains a simple sensor setup and avoids the complexity of stereo or global shutter configurations. Experimental results demonstrate that Cross-Fusion achieves localization accuracy comparable to GPS-based methods and performs reliably in challenging feature-sparse environments.
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