Assessing geoaccuracy of structure from motion point clouds from long-range image collections
Assessing geoaccuracy of structure from motion point clouds from long-range image collections
复制标题
根据远程图像集合的运动点云评估结构的地理精度
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
C. Salvaggio
中科院分区：
文献类型：
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作者：
David Nilosek;D. Walvoord;C. Salvaggio
抽象的。从机载平台自动提取和精确生成场景结构是摄影测量、遥感和计算机视觉领域的一个重要应用目标。传统上，这种结构是通过运动结构(SfM)工作流自动提取的。尽管这一过程非常强大，但精度误差的分析可能会被证明是困难的。我们的工作提出了一种分析SfM派生的点云的地理配准误差的方法，这些点云已经转换到固定的地面坐标系。误差分析是使用合成机载图像进行的，它为每幅图像中每个像素的射线-表面交点提供了绝对真实的信息。评估了三种地理配准方法：(1)使用全球定位系统(GPS)相机中心，(2)直接使用来自机载导航仪器的位姿信息，以及(3)使用最近开发的方法，该方法利用正投影函数和基于SFM的相机位姿估计。研究发现，基于GPS相机中心的地理配准和直接使用来自机载导航仪器的位姿信息，对来自SfM过程和仪器的噪声非常敏感。使用前向投影函数计算的地理配准变换和派生的位姿估计被证明对这些误差具有更强的鲁棒性。
Abstract. Automatically extracted and accurate scene structure generated from airborne platforms is a goal of many applications in the photogrammetry, remote sensing, and computer vision fields. This structure has traditionally been extracted automatically through the structure-from-motion (SfM) workflows. Although this process is very powerful, the analysis of error in accuracy can prove difficult. Our work presents a method of analyzing the georegistration error from SfM derived point clouds that have been transformed to a fixed Earth-based coordinate system. The error analysis is performed using synthetic airborne imagery which provides absolute truth for the ray-surface intersection of every pixel in every image. Three methods of georegistration are assessed; (1) using global positioning system (GPS) camera centers, (2) using pose information directly from on-board navigational instrumentation, and (3) using a recently developed method that utilizes the forward projection function and SfM-derived camera pose estimates. It was found that the georegistration derived from GPS camera centers and the direct use of pose information from on-board navigational instruments is very sensitive to noise from both the SfM process and instrumentation. The georegistration transform computed using the forward projection function and the derived pose estimates prove to be far more robust to these errors.