用多传感器融合加速大范围航拍正射影像生成
A Multi-Sensor Fusion Approach for Rapid Orthoimage Generation in Large-Scale UAV Mapping
- 基于先验姿态优化特征匹配，提升速度与精度
- 低纹理农田场景下仍保持稳定匹配性能
- 适合需要快速生成正射影像的农业监测场景
从无人机（UAV）快速生成大范围正射影像一直是航空测绘领域的研究重点。本文提出一种集成全球定位系统（GPS）、惯性测量单元（IMU）、4D毫米波雷达和相机的多传感器无人机系统，以解决传统正射影像生成方法在时间效率、系统鲁棒性和地理参考精度方面的局限。提出一种先验姿态优化的特征匹配方法，显著提升匹配速度与准确率，减少所需特征数量，并为结构光从运动（SfM）过程提供精确参考。实验表明，该方法在低纹理场景（如农田）中表现稳健，可在短时间内实现高精度正射影像生成，有效支持农田检测与管理。
原文摘要 · Abstract (English)
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.
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