Rapid generation of large-scale orthoimages from Unmanned Aerial Vehicles (UAVs) has been a long-standing focus of research in the field of aerial mapping. A multi-sensor UAV system, integrating the Global Positioning System (GPS), Inertial Measurement Unit (IMU), 4D millimeter-wave radar and camera, can provide an effective solution to this problem. In this paper, we utilize multi-sensor data to overcome the limitations of conventional orthoimage generation methods in terms of temporal performance, system robustness, and geographic reference accuracy. A prior-pose-optimized feature matching method is introduced to enhance matching speed and accuracy, reducing the number of required features and providing precise references for the Structure from Motion (SfM) process. The proposed method exhibits robustness in low-texture scenes like farmlands, where feature matching is difficult. Experiments show that our approach achieves accurate feature matching orthoimage generation in a short time. The proposed drone system effectively aids in farmland detection and management.

翻译：从无人机快速生成大规模正射影像长期以来一直是航空测绘领域的研究重点。集成全球定位系统、惯性测量单元、4D毫米波雷达与相机的多传感器无人机系统，可为该问题提供有效的解决方案。本文利用多传感器数据，克服了传统正射影像生成方法在时效性、系统鲁棒性和地理参考精度方面的局限。引入了一种先验位姿优化的特征匹配方法，以提升匹配速度与精度，减少所需特征数量，并为运动恢复结构过程提供精确参考。所提方法在农田等纹理匮乏、特征匹配困难的场景中表现出良好的鲁棒性。实验表明，我们的方法能够在短时间内实现精确的特征匹配与正射影像生成。所提出的无人机系统可有效辅助农田检测与管理。