无需降采样即可实时处理超高清无人机影像的密集匹配方法
Real-Time Bundle Adjustment for Ultra-High-Resolution UAV Imagery Using Adaptive Patch-Based Feature Tracking
- 将图像分块动态跟踪，利用导航与数字高程模型保证特征一致性
- 在5000万像素影像上实现2秒内完成全局光束法平差，无需GPU
- 适合灾害应急、基础设施监测等需要实时大范围测绘的场景
实时处理无人机影像对灾后响应等需要快速地理信息的应用至关重要。然而，高分辨率影像的实时处理面临特征提取、匹配和光束法平差（BA）的巨大计算压力。传统方法要么降低图像分辨率损失细节，要么耗时过长。为此，我们提出一种新型实时BA框架，直接在全分辨率影像上运行。该轻量级、可部署于机载系统的方案将图像划分为用户定义的块（如150×150像素网格），结合无人机GNSS/IMU数据与粗略全球数字地表模型（DSM），实现跨帧块的动态追踪，确保特征提取与匹配的空间一致性。通过实时获取无人机导航系统数据，快速确定图像间重叠关系，仅对局部相邻影像簇（含相邻航带）进行优化，实现实时性能并保持全局BA精度。该算法已集成至德国航空航天中心模块化航空相机系统（MACS），支持灾害响应、基础设施监测和海岸保护中的实时大范围制图。在包含5000万像素影像的MACS数据集上验证表明，该方法可在无GPU加速下实现2秒内完成完整光束法平差，保持精确相机姿态与高保真地图重建。
原文摘要 · Abstract (English)
Real-time processing of UAV imagery is crucial for applications requiring urgent geospatial information, such as disaster response, where rapid decision-making and accurate spatial data are essential. However, processing high-resolution imagery in real time presents significant challenges due to the computational demands of feature extraction, matching, and bundle adjustment (BA). Conventional BA methods either downsample images, sacrificing important details, or require extensive processing time, making them unsuitable for time-critical missions. To overcome these limitations, we propose a novel real-time BA framework that operates directly on fullresolution UAV imagery without downsampling. Our lightweight, onboard-compatible approach divides each image into user-defined patches (e.g., NxN grids, default 150x150 pixels) and dynamically tracks them across frames using UAV GNSS/IMU data and a coarse, globally available digital surface model (DSM). This ensures spatial consistency for robust feature extraction and matching between patches. Overlapping relationships between images are determined in real time using UAV navigation system, enabling the rapid selection of relevant neighbouring images for localized BA. By limiting optimization to a sliding cluster of overlapping images, including those from adjacent flight strips, the method achieves real-time performance while preserving the accuracy of global BA. The proposed algorithm is designed for seamless integration into the DLR Modular Aerial Camera System (MACS), supporting largearea mapping in real time for disaster response, infrastructure monitoring, and coastal protection. Validation on MACS datasets with 50MP images demonstrates that the method maintains precise camera orientations and high-fidelity mapping across multiple strips, running full bundle adjustment in under 2 seconds without GPU acceleration.
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