Fast automatic registration of range images from 3D imaging systems using sphere targets

Abstract The use of 3D imaging systems (e.g., laser scanners) in construction has grown significantly in the past decade. Range images acquired with such systems often require registration. This paper describes an automatic method to rapidly locate spheres and perform a registration based on three pairs of matching points (centers of fitted spheres) in two range images. The proposed method is directly applicable for regularly gridded datasets obtained with instruments that are typically used for construction applications and whose maximum ranges are greater than 50 m. A lab was scanned from two locations at three different scan densities. Four spheres were located in the lab, and the total number of points hitting the four spheres was a small fraction ( 6 and 3.4 × 10 6 points is obtained in less than 30 s. At the medium scan density, two range images with 1.6 × 10 6 and 0.8 × 10 6 points can be registered in less than 2 s.

[1]  Geraldine S. Cheok,et al.  Recovering Circles and Spheres from Point Data , 2006 .

[2]  M. Devrim Mehmet Devrim Akça.  FULL AUTOMATIC REGISTRATION OF LASER SCANNER POINT CLOUDS , 2003 .

[3]  Geraldine S. Cheok,et al.  Effect of scanning density on the accuracy of locating sphere centers for registration , 2008 .

[4]  Hans-Jürgen Warnecke,et al.  Least-squares orthogonal distances fitting of circle, sphere, ellipse, hyperbola, and parabola , 2001, Pattern Recognit..

[5]  Kostas Daniilidis,et al.  Fully Automatic Registration of 3D Point Clouds , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[6]  Jack Snoeyink,et al.  Fitting a Set of Points by a Circle , 1998, Discret. Comput. Geom..

[7]  William H. Press,et al.  Numerical recipes in C , 2002 .

[8]  Bruce L. Golden,et al.  Perspectives in Operations Research , 2006 .

[9]  H. Späth,et al.  Least-Square Fitting with Spheres , 1998 .

[10]  Supun Samarasekera,et al.  Automatic registration and visualization of occluded targets using ladar data , 2003, SPIE Defense + Commercial Sensing.

[11]  Ioannis Stamos,et al.  Automated feature-based range registration of urban scenes of large scale , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[12]  Martial Hebert,et al.  Fully automatic registration of multiple 3D data sets , 2003, Image Vis. Comput..

[13]  Soon-Wook Kwon,et al.  Fitting range data to primitives for rapid local 3D modeling using sparse range point clouds , 2004 .

[14]  Jonathan Lawry,et al.  Classification of Weather Radar Images using Linguistic Decision Trees with Conditional Labelling , 2007, 2007 IEEE International Fuzzy Systems Conference.

[15]  Christina Grönwall.  Ground Object Recognition using Laser Radar Data : Geometric Fitting, Performance Analysis, and Applications , 2006 .

[16]  Claus Brenner,et al.  Registration of terrestrial laser scanning data using planar patches and image data , 2006 .

[17]  Daniel S. Clouse,et al.  Estimating the position of a sphere from range images , 2002, Proceedings, IEEE Aerospace Conference.

[18]  Ralph R. Martin,et al.  Robust Segmentation of Primitives from Range Data in the Presence of Geometric Degeneracy , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Frédéric Bosché,et al.  Automated retrieval of 3D CAD model objects in construction range images , 2008 .

[21]  I. D. Coope,et al.  Circle fitting by linear and nonlinear least squares , 1993 .

[22]  W. Gander,et al.  Least-squares fitting of circles and ellipses , 1994 .

[23]  I. Kasa.  A circle fitting procedure and its error analysis , 1976, IEEE Transactions on Instrumentation and Measurement.

[24]  Yuriy Reshetyuk,et al.  Investigation and calibration of pulsed time-of-flight terrestrial laser scanners , 2006 .

[25]  Ioannis Stamos,et al.  Geometry and Texture Recovery of Scenes of Large Scale , 2002, Comput. Vis. Image Underst..

[26]  D. Lichti,et al.  CLASSIFICATION AND SEGMENTATION OF TERRESTRIAL LASER SCANNER POINT CLOUDS USING LOCAL VARIANCE INFORMATION , 2006 .

[27]  Adam Krzyzak,et al.  Robust Estimation for Range Image Segmentation and Reconstruction , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Dale Umbach,et al.  A few methods for fitting circles to data , 2003, IEEE Trans. Instrum. Meas..

[29]  T WhitakerRoss,et al.  A Maximum-Likelihood Surface Estimator for Dense Range Data , 2002 .

[30]  Josef Kittler,et al.  Adaptive Texture Representation Methods for Automatic Target Recognition , 1999, BMVC.

[31]  Mark R. Stevens,et al.  Toward Target Verification Through 3-D Model-Based Sensor Fusion , 1996 .

[32]  William C. Stone,et al.  Performance Analysis of Next-Generation LADAR for Manufacturing , 2004 .

[33]  T Rabbani Shah,et al.  Automatic point cloud registration using constrained search for corresponding objects , 2005 .

[34]  Craig M. Shakarji,et al.  Least-Squares Fitting Algorithms of the NIST Algorithm Testing System , 1998, Journal of research of the National Institute of Standards and Technology.

[35]  Ozy Sjahputera,et al.  Fuzzy scene matching in LADAR imagery , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).