Point cloud registration using congruent pyramids

We present a method to compute an initial alignment for pairwise registration of point clouds. This method uses the properties of a rigid body transformation - the ratio of lengths is preserved, the euclidean distance between points is preserved - to find congruent pyramids in two point clouds. The corresponding vertices of the congruent pyramids are used to derive a closed form solution for initial alignment. The alignment is refined further using the Iterative Closest Point algorithm. We validate the method on challenging datasets - which include airborne LIDAR, outdoor, and indoor - having initial offsets and varying densities.

[1]  Tim Bailey,et al.  Scan segments matching for pairwise 3D alignment , 2012, 2012 IEEE International Conference on Robotics and Automation.

[2]  Andreas Birk,et al.  Spectral registration of noisy sonar data for underwater 3D mapping , 2011, Auton. Robots.

[3]  Daniel Cohen-Or,et al.  4-points congruent sets for robust pairwise surface registration , 2008, ACM Trans. Graph..

[4]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

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

[6]  Joachim Hertzberg,et al.  6D SLAM—3D mapping outdoor environments , 2007, J. Field Robotics.

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

[8]  Paolo Cignoni,et al.  MeshLab: an Open-Source 3D Mesh Processing System , 2008, ERCIM News.

[9]  K. S. Arun,et al.  Least-Squares Fitting of Two 3-D Point Sets , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Joachim Hertzberg,et al.  Evaluation of 3D registration reliability and speed - A comparison of ICP and NDT , 2009, 2009 IEEE International Conference on Robotics and Automation.

[11]  Igor Guskov,et al.  Multi-scale features for approximate alignment of point-based surfaces , 2005, SGP '05.

[12]  Ioannis Stamos,et al.  Range Image Registration Based on Circular Features , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[13]  Yu Zhong,et al.  Intrinsic shape signatures: A shape descriptor for 3D object recognition , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[14]  Geraldine S. Cheok,et al.  Fast automatic registration of range images from 3D imaging systems using sphere targets , 2009 .

[15]  Srikanth Saripalli,et al.  3D change detection using low cost aerial imagery , 2012, 2012 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

[16]  Martin Magnusson,et al.  The three-dimensional normal-distributions transform : an efficient representation for registration, surface analysis, and loop detection , 2009 .

[17]  Roland Siegwart,et al.  Comparing ICP variants on real-world data sets , 2013, Auton. Robots.

[18]  NüchterAndreas,et al.  6D SLAM3D mapping outdoor environments , 2007 .

[19]  Mauro R. Ruggeri,et al.  Robust surface registration using N-points approximate congruent sets , 2011, EURASIP J. Adv. Signal Process..

[20]  Radu Bogdan Rusu,et al.  3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.

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

[22]  Kok-Lim Low Linear Least-Squares Optimization for Point-to-Plane ICP Surface Registration , 2004 .