Yuan Xia, Wang Kaiyang, Qin Riyu, Xu Jiachen
Nanjing University of Science and Technology, School of Computer Science and Engineering, Nanjing, 210094, China.
Dahua Technology, Software Development Department, Hangzhou, 310000, China.
Sci Data. 2025 May 7;12(1):754. doi: 10.1038/s41597-025-05075-9.
High-precision localization is critical for intelligent robotics in autonomous driving, smart agriculture, and military operations. While Global Navigation Satellite System (GNSS) provides global positioning, its reliability deteriorates severely in signal degraded environments like urban canyons. Cross-view pose estimation using aerial-ground sensor fusion offers an economical alternative, yet current datasets lack field scenarios and high-resolution LiDAR support.This work introduces a multimodal cross-view dataset addressing these gaps. It contains 29,940 synchronized frames across 11 operational environments (6 field environments, 5 urban roads), featuring: 1) 144-channel LiDAR point clouds, 2) ground-view RGB images, and 3) aerial orthophotos. Centimeter-accurate georeferencing is ensured through GNSS fusion and post-processed kinematic positioning. The dataset uniquely integrates field environments and high-resolution LiDAR-aerial-ground data triplets, enabling rigorous evaluation of 3-DoF pose estimation algorithms for orientation alignment and coordinate transformation between perspectives.This resource supports development of robust localization systems for field robots in GNSS-denied conditions, emphasizing cross-view feature matching and multisensor fusion. Light Detection And Ranging (LiDAR)-enhanced ground truth further distinguishes its utility for complex outdoor navigation research.
高精度定位对于自动驾驶、智能农业和军事行动中的智能机器人至关重要。虽然全球导航卫星系统（GNSS）提供全球定位，但在城市峡谷等信号退化环境中，其可靠性会严重下降。使用空地传感器融合的跨视角姿态估计提供了一种经济的替代方案，但目前的数据集缺乏实地场景和高分辨率激光雷达支持。这项工作引入了一个多模态跨视角数据集来弥补这些差距。它包含11个操作环境（6个野外环境、5条城市道路）中的29,940个同步帧，其特点包括：1）144通道激光雷达点云，2）地面视角RGB图像，以及3）航空正射影像。通过GNSS融合和后处理运动定位确保厘米级精确地理配准。该数据集独特地整合了野外环境和高分辨率激光雷达-空地数据三元组，能够对用于视角之间方向对齐和坐标转换的3自由度姿态估计算法进行严格评估。此资源支持在GNSS受限条件下为野外机器人开发强大的定位系统，强调跨视角特征匹配和多传感器融合。激光探测与测距（LiDAR）增强的地面真值进一步凸显了其在复杂户外导航研究中的效用。
