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通过无人机相机图像采集实现大范围物体的先进三维摄影测量表面重建。

Advanced 3D Photogrammetric Surface Reconstruction of Extensive Objects by UAV Camera Image Acquisition.

机构信息

Department of Electric, Electronics and Computer Engineering, University of Catania, V.le A. Doria, 6, 95125 Catania, Italy.

Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, via Marengo 2, 09123 Cagliari, Italy.

出版信息

Sensors (Basel). 2018 Aug 26;18(9):2815. doi: 10.3390/s18092815.

DOI:10.3390/s18092815
PMID:30149688
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6163311/
Abstract

This paper proposes a replicable methodology to enhance the accuracy of the photogrammetric reconstruction of large-scale objects based on the optimization of the procedures for Unmanned Aerial Vehicle (UAV) camera image acquisition. The relationships between the acquisition grid shapes, the acquisition grid geometric parameters (pitches, image rates, camera framing, flight heights), and the 3D photogrammetric surface reconstruction accuracy were studied. Ground Sampling Distance (), the necessary number of photos to assure the desired overlapping, and the surface reconstruction accuracy were related to grid shapes, image rate, and camera framing at different flight heights. The established relationships allow to choose the best combination of grid shapes and acquisition grid geometric parameters to obtain the desired accuracy for the required . This outcome was assessed by means of a case study related to the ancient arched brick in Adrano (Sicily, Italy). The reconstruction of the three-dimensional surfaces of this structure, obtained by the efficient Structure-From-Motion (SfM) algorithms of the commercial software Pix4Mapper, supported the study by validating it with experimental data. A comparison between the surface reconstruction with different acquisition grids at different flight heights and the measurements obtained with a 3D terrestrial laser and total station-theodolites allowed to evaluate the accuracy in terms of Euclidean distances.

摘要

本文提出了一种可复制的方法，通过优化无人机 (UAV) 相机图像采集的程序，提高大规模物体摄影测量重建的准确性。研究了采集网格形状、采集网格几何参数（俯仰角、图像率、相机取景、飞行高度）与三维摄影测量表面重建精度之间的关系。地面采样距离（）、保证所需重叠的必要照片数量以及表面重建精度与不同飞行高度下的网格形状、图像率和相机取景有关。所建立的关系允许选择最佳的网格形状和采集网格几何参数组合，以获得所需的精度和所需的。通过与意大利西西里岛阿德拉诺的古老拱形砖的案例研究评估了这一结果。通过商业软件 Pix4Mapper 的高效运动结构 (SfM) 算法对该结构的三维表面进行重建，并用实验数据对其进行了验证，支持了这项研究。通过比较不同飞行高度和不同采集网格的表面重建与使用三维地面激光和全站仪 - 经纬仪获得的测量结果，可以评估欧几里得距离方面的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/f7bf10684bb7/sensors-18-02815-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/1683a636e945/sensors-18-02815-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/541c09c8b1f3/sensors-18-02815-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/ff6bdd090efc/sensors-18-02815-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/1f68de1954b3/sensors-18-02815-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/311d41b469ad/sensors-18-02815-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/09ecbd7a1bad/sensors-18-02815-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/5f2efabc067f/sensors-18-02815-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/f8120761662c/sensors-18-02815-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/3d579d5e8d18/sensors-18-02815-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/4ddb770d053d/sensors-18-02815-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/0758a8cd145b/sensors-18-02815-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/09e61d1980d3/sensors-18-02815-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/e02c0a60dcd6/sensors-18-02815-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/f7bf10684bb7/sensors-18-02815-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/1683a636e945/sensors-18-02815-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/541c09c8b1f3/sensors-18-02815-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/ff6bdd090efc/sensors-18-02815-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/1f68de1954b3/sensors-18-02815-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/311d41b469ad/sensors-18-02815-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/09ecbd7a1bad/sensors-18-02815-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/5f2efabc067f/sensors-18-02815-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/f8120761662c/sensors-18-02815-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/3d579d5e8d18/sensors-18-02815-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/4ddb770d053d/sensors-18-02815-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/0758a8cd145b/sensors-18-02815-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/09e61d1980d3/sensors-18-02815-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/e02c0a60dcd6/sensors-18-02815-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54ff/6163311/f7bf10684bb7/sensors-18-02815-g014.jpg

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