Photogrammetric error sources and impacts on modeling and surveying in construction engineering applications

This paper reviews photogrammetric error sources and their impacts on modeling and surveying for construction quantity takeoff, quality control, and site safety monitoring applications. These error sources include camera internal parameters (i.e., type, principal point, principal distance, and camera lens distortion coefficients), imaging settings (i.e., shooting distances, baselines, percentage of photo overlaps, number of overlapping photos, camera intersection angles, and angles of incidence), and processing software programs. To augment the body of knowledge on photogrammetric modeling errors, this paper further conducts experiment, which concerns characterization of the behavior of different strategies in selecting reference lines for fixing absolute scale of photogrammetric models. In construction photogrammetric surveying, it is imperative to convert the relative scale of a 3D model into absolute measurements so geometric measurements can be taken. Previous work suggests this can be done through the determination of a reference line in absolute units; however, the position and quantity of reference lines has not been investigated. This experiment attempts to tackle this issue. The result shows that one horizontal reference line in the middle of the object performed with consistent accuracy, but if a specific area on the object needs more accurate measurements, it is best to select a reference line in that area. The review and the experimental findings may help construction professionals better understand the performance of the photogrammetric surveying and apply it in their real-world projects.

[1]  Pedro Arias,et al.  Verification artifact for photogrammetric measurement systems , 2011 .

[2]  Jean-Yves Bouguet,et al.  Camera calibration toolbox for matlab , 2001 .

[3]  H. Wegmann.  Image Orientation by Combined ( A ) AT with GPS and IMU , 2002 .

[4]  Wu Chen,et al.  Tunnel-Boring Machine Positioning during Microtunneling Operations through Integrating Automated Data Collection with Real-Time Computing , 2011 .

[5]  Fei Dai,et al.  Assessing the Accuracy of Applying Photogrammetry to Take Geometric Measurements on Building Products , 2010 .

[6]  Duane C. Brown,et al.  Close-Range Camera Calibration , 1971 .

[7]  X. Wang,et al.  The Principal Point and CCD Cameras , 1998 .

[8]  M. Sveda,et al.  Industrial Measurement Application Development Using Case-Based Reasoning , 2007, Second International Conference on Systems (ICONS'07).

[9]  Burcu Akinci,et al.  Automated Recognition of 3D CAD Objects in Site Laser Scans for Project 3D Status Visualization and Performance Control , 2009 .

[10]  Clive S. Fraser,et al.  Digital camera self-calibration , 1997 .

[11]  Mani Golparvar-Fard,et al.  Development and Use of the NIST Intelligent and Automated Construction Job Site Testbed | NIST , 2011 .

[12]  Zhengyou Zhang,et al.  Flexible camera calibration by viewing a plane from unknown orientations , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  Thomas Luhmann,et al.  Precision potential of photogrammetric 6DOF pose estimation with a single camera , 2009 .

[14]  C. Fraser,et al.  Digital camera calibration methods: Considerations and comparisons , 2006 .

[15]  Burcin Becerik-Gerber,et al.  Imaged-based verification of as-built documentation of operational buildings , 2012 .

[16]  S. El-Hakim,et al.  Critical Factors and Configurations for Practical 3 D Image-Based Modeling , 2003 .

[17]  Martin J. Smith,et al.  ABSOLUTE AND EXTERIOR ORIENTATION USING LINEAR FEATURES , 2000 .

[18]  Ming Lu,et al.  Photogrammetry Assisted Measurement of Interstory Drift for Rapid Post-disaster Building Damage Reconnaissance , 2011 .

[19]  Mani Golparvar-Fard,et al.  Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques , 2011 .

[20]  Emmanuel P. Baltsavias,et al.  A comparison between photogrammetry and laser scanning , 1999 .

[21]  Patricio A. Vela,et al.  Monocular Videogrammetry for Generating Absolute-Scale 3D Point Cloud Data of Civil and Infrastructure Scenes , 2013 .

[22]  Fei Dai,et al.  Comparison of Image-Based and Time-of-Flight-Based Technologies for Three-Dimensional Reconstruction of Infrastructure , 2013 .

[23]  Ming Lu,et al.  Photo-based 3D modeling of construction resources for visualization of operations simulation: Case of modeling a precast façade , 2008, 2008 Winter Simulation Conference.

[24]  Fernanda Leite,et al.  Evaluation of accuracy of as-built 3D modeling from photos taken by handheld digital cameras , 2012 .

[25]  Roland Bless,et al.  Network Design , 2011, 4WARD Project.

[26]  J. Chandler,et al.  A convergent image configuration for DEM extraction that minimises the systematic effects caused by an inaccurate lens model , 2008 .

[27]  Edward J. Jaselskis,et al.  Improving transportation projects using laser scanning , 2005 .

[28]  Thomas Luhmann,et al.  Close range photogrammetry for industrial applications , 2010 .

[29]  J. Chandler,et al.  Minimising systematic error surfaces in digital elevation models using oblique convergent imagery , 2011 .

[30]  Burcu Akinci,et al.  A formalism for utilization of sensor systems and integrated project models for active construction quality control , 2006 .

[31]  Dirk Rieke-Zapp,et al.  Evaluation of the geometric stability and the accuracy potential of digital cameras - Comparing mechanical stabilisation versus parameterisation , 2009 .

[32]  育久 満上,et al.  Bundler: Structure from Motion for Unordered Image Collections , 2011 .

[33]  Ioannis Brilakis,et al.  Comparison of Optical Sensor-Based Spatial Data Collection Techniques for Civil Infrastructure Modeling , 2009 .

[34]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[35]  Stuart Robson,et al.  PRINCIPAL POINT BEHAVIOUR AND CALIBRATION PARAMETER MODELS FOR KODAK DCS CAMERAS , 1998 .

[36]  Thomas S. Huang,et al.  BOOK REVIEW: Calibration and Orientation of Cameras in Computer Vision , 2001 .

[37]  K. Atkinson.  Close Range Photogrammetry and Machine Vision , 1996 .

[38]  J. Hothmer.  Book reviewInternational archives of photogrammetry and remote sensing: ISPRS, Editor S. Murai: volume 27 part A, Tokyo-Japan-Japan 1989 , 1989 .

[39]  Stuart Robson,et al.  Close Range Photogrammetry: Principles, Methods and Applications , 2006 .

[40]  Burcu Akinci,et al.  Characterization of Laser Scanners and Algorithms for Detecting Flatness Defects on Concrete Surfaces , 2011, J. Comput. Civ. Eng..

[41]  Carlo Tomasi.  Camera Calibration , 2002 .

[42]  Madhubalan Viswanathan,et al.  Measurement error and research design , 2005 .

[43]  Janne Heikkilä,et al.  A four-step camera calibration procedure with implicit image correction , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[44]  Horst A. Beyer,et al.  System Calibration Through Self-Calibration , 2001 .

[45]  Ioannis Brilakis,et al.  Comparison of Civil Infrastructure Optical-based Spatial Data Acquisition Techniques , 2007 .

[46]  Jan-Michael Frahm,et al.  Detailed Real-Time Urban 3D Reconstruction from Video , 2007, International Journal of Computer Vision.

[47]  Ming Lu,et al.  Three-Dimensional Modeling of Site Elements by Analytically Processing Image Data Contained in Site Photos , 2013 .

[48]  Ioannis Brilakis,et al.  Progressive 3D reconstruction of infrastructure with videogrammetry , 2011 .