Proceedings Article10.1049/CP.2010.1351
Accuracy evaluation method and experiments for photogrammetry based on 3D reference field
Dong Mingli,Wang Jun,Yan Bixi,Lou Xiaoping,Chen Ruibao +4 more
- 01 Jan 2010
- pp 489-492
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TL;DR: In this article, a new accuracy evaluation method for photogrammetry system based on 3D reference field is proposed, and the effect of optical axes angles and base lines is studied.
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Abstract: Photogrammetry is a task-oriented and multi-stage measurement metrology and its accuracy is affected by many factors, which is the most important and difficult point in this field A new accuracy evaluation method for photogrammetry system based on 3D reference field is proposed in this paper, and the effect of optical axes angles and base lines is studied The 3D reference technology for this accuracy evaluation method which can provide the conversion between laser tracking system and photogrammetry is designed and built The stability experiments of the reference field are carried out and the results show that the repeatability of point measurement reaches 1 μm and satisfies the requirement According to the accuracy evaluation method and experiments based on the 3D reference field, the relationships between the system error and optical axes angles, base lines are established, which will guide the design and implementation of measurement schemes
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Citations
Accuracy analysis of point cloud modeling for evaluating concrete specimens
Nicolas D'Amico,Tzuyang Yu +1 more
TL;DR: Geometric accuracy of photogrammetric modeling is investigated by studying the effects of number of photos, radius of curvature, and point cloud density (PCD) on estimated lengths, areas, volumes, and different stress states of concrete cylinders and panels, and it was found that the increase of numbers of photos does not necessarily improve the geometric accuracy of point cloud models (PCM).
7
A Machine Learning Framework for Building Passive Surveillance Photogrammetry Models
Eric Sturzinger,Bradley L. Whitehall,James C. Tyler,Christopher J. Lowrance +3 more
- 01 Apr 2019
TL;DR: This paper investigates the accuracy of building photogrammetric models using machine learning by using an unmanned ground vehicle (UGV) that moved throughout the fields of view of multiple cameras to build a nonlinear, multitarget prediction model.
1
References
Accuracy analysis of point cloud modeling for evaluating concrete specimens
Nicolas D'Amico,Tzuyang Yu +1 more
TL;DR: Geometric accuracy of photogrammetric modeling is investigated by studying the effects of number of photos, radius of curvature, and point cloud density (PCD) on estimated lengths, areas, volumes, and different stress states of concrete cylinders and panels, and it was found that the increase of numbers of photos does not necessarily improve the geometric accuracy of point cloud models (PCM).
7
A Machine Learning Framework for Building Passive Surveillance Photogrammetry Models
Eric Sturzinger,Bradley L. Whitehall,James C. Tyler,Christopher J. Lowrance +3 more
- 01 Apr 2019
TL;DR: This paper investigates the accuracy of building photogrammetric models using machine learning by using an unmanned ground vehicle (UGV) that moved throughout the fields of view of multiple cameras to build a nonlinear, multitarget prediction model.
1