A Structure-Based Registration Method for Terrestrial Laser Scanning Data

This paper presents a novel structure-based registration method for terrestrial laser scanning (TLS) data. The line support region (LSR), which fits the 3D line segment, is adopted to describe the scene structure and reduce geometric complexity. Then we employ an evolution computation method to solve the optimization problem of global registration. Our method can be further enhanced by iterative closest points (ICP) or other local registration methods. We demonstrate the robustness of our algorithm on several point cloud sets with varying extent of overlap and degree of noise.

[1]  Jeffrey Horn,et al.  Handbook of evolutionary computation , 1997 .

[2]  Oscar Cordón,et al.  A fast and accurate approach for 3D image registration using the scatter search evolutionary algorithm , 2006, Pattern Recognit. Lett..

[3]  Sandy Irani,et al.  Combinatorial and experimental results for randomized point matching algorithms , 1996, SCG '96.

[4]  Tong Lee,et al.  Surface registration using a dynamic genetic algorithm , 2004, Pattern Recognit..

[5]  Gérard G. Medioni,et al.  Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

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

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

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

[9]  Reinhard Klein,et al.  Efficient RANSAC for Point‐Cloud Shape Detection , 2007, Comput. Graph. Forum.

[10]  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..

[11]  Stefan Hinz,et al.  Fast and automatic image-based registration of TLS data , 2011 .

[12]  Franck Patrick Vidal,et al.  Tuning of Patient-Specific Deformable Models Using an Adaptive Evolutionary Optimization Strategy , 2012, IEEE Transactions on Biomedical Engineering.

[13]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[14]  Cheng Wang,et al.  Line segment extraction for large scale unorganized point clouds , 2015 .

[15]  Nicolas David,et al.  Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge , 2013 .

[16]  Andriy Myronenko,et al.  Point Set Registration: Coherent Point Drift , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Niloy J. Mitra,et al.  Super4PCS: Fast Global Pointcloud Registration via Smart Indexing , 2019 .

[18]  Darius Burschka,et al.  Stochastic global optimization for robust point set registration , 2011, Comput. Vis. Image Underst..

[19]  John Holland,et al.  Adaptation in Natural and Artificial Sys-tems: An Introductory Analysis with Applications to Biology , 1975 .

[20]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..