跨会话3D LiDAR与相机融合用于GPS拒止环境下无人机的鲁棒定位
Cross-Session 3D LiDAR and Camera Fusion for Robust Localization of Unmanned Aerial Vehicles in GPS-Denied Environments
AI总结：
提出Cross-Fusion方法，融合3D LiDAR和单目相机数据，通过跨会话融合策略整合多代理基线调查数据，实现GPS拒止环境下无人机的实时高精度定位。
摘要：
无人机的精确定位对于结构健康监测等应用至关重要，尤其是在全球定位系统（GPS）信号被拒止或不可靠的环境中，例如室内空间、隧道、城市峡谷或大型结构下方区域。为了解决这一挑战，我们提出了Cross-Fusion，一种新颖的实时无人机定位方法，该方法集成了来自3D光检测与测距（LiDAR）和单目相机的数据。其关键贡献在于跨会话融合策略，该策略整合了在常规基线调查期间从多个代理收集的视觉和几何信息，以提高定位一致性和地图完整性。该系统采用基于LiDAR的里程计进行运动跟踪，并通过单个红绿蓝（RGB）相机进行基于图像的特征匹配，以校正漂移并提高精度。与视觉惯性系统不同，Cross-Fusion保持了简单的传感器设置，避免了立体或全局快门配置的复杂性。实验结果表明，Cross-Fusion实现了与基于GPS的方法相当的定位精度，并在具有挑战性的特征稀疏环境中可靠地工作。
英文摘要：
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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