Registration of partially overlapping laser-radar range images

Abstract. To register partially overlapping three-dimensional point sets from different viewpoints, it is necessary to remove spurious corresponding point pairs that are not located in overlapping regions. Most variants of the iterative closest point (ICP) algorithm require users to manually select the rejection parameters for discarding spurious point pairs between the registering views. This requirement often results in unreliable and inaccurate registration. To overcome this problem, we present an improved ICP algorithm that can automatically determine the rejection percentage to reliably and accurately align partially overlapping laser-radar (ladar) range images. The similarity of k neighboring features of each nonplanar point is employed to determine reasonable point pairs in nonplanar regions, and the distance measurement method is used to find reasonable point pairs in planar regions. The rejection percentage can be obtained from these two sets of reasonable pairs. The performance of our algorithm is compared with that of five other algorithms using various models with low and high curvatures. The experimental results show that our algorithm is more accurate and robust than the other algorithms.

[1]  Gaurav S. Sukhatme,et al.  3D tree reconstruction from laser range data , 2009, 2009 IEEE International Conference on Robotics and Automation.

[2]  Barry L. Stann,et al.  Simulation-based analysis of range and cross-range resolution requirements for the identification of vehicles in ladar imagery , 2003 .

[3]  Zhengyou Zhang,et al.  Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.

[4]  Xiaoping Lou,et al.  Automatic 3D point clouds registration method , 2010, SPIE/COS Photonics Asia.

[5]  Yu Meng,et al.  Registration of point clouds using sample-sphere and adaptive distance restriction , 2011, The Visual Computer.

[6]  Jon Louis Bentley,et al.  An Algorithm for Finding Best Matches in Logarithmic Expected Time , 1977, TOMS.

[7]  Derek D. Lichti,et al.  A method for automated registration of unorganised point clouds , 2008 .

[8]  David Fofi,et al.  A review of recent range image registration methods with accuracy evaluation , 2007, Image Vis. Comput..

[9]  Katsuhiko Sakaue,et al.  Registration and integration of multiple range images for 3-D model construction , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[10]  Chitra Dorai,et al.  COSMOS - A Representation Scheme for 3D Free-Form Objects , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

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

[12]  Wolfram Burgard,et al.  Instace-Based AMN Classification for Improved Object Recognition in 2D and 3D Laser Range Data , 2007, IJCAI.

[13]  Qi Wang,et al.  Random subspace ensemble for target recognition of ladar range image , 2013 .

[14]  Kari Pulli,et al.  Multiview registration for large data sets , 1999, Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062).

[15]  Gustav Tolt,et al.  Spatial filtering for detection of partly occluded targets , 2011 .

[16]  Mohammed Bennamoun,et al.  3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Huy Tho Ho,et al.  Curvature-based approach for multi-scale feature extraction from 3D meshes and unstructured point clouds , 2009, DICTA 2009.