Large Common Plansets-4-Points Congruent Sets for Point Cloud Registration

Point cloud registration combines multiple point cloud data sets collected from different positions using the same or different devices to form a single point cloud within a single coordinate system. Point cloud registration is usually achieved through spatial transformations that align and merge multiple point clouds into a single globally consistent model. In this paper, we present a new segmentation-based approach for point cloud registration. Our method consists of extracting plane structures from point clouds and then, using the 4-Point Congruent Sets (4PCS) technique, we estimate transformations that align the plane structures. Instead of a global alignment using all the points in the dataset, our method aligns 2-point clouds using their local plane structures. This considerably reduces the data size, computational workload, and execution time. Unlike conventional methods that seek to align the largest number of common points between entities, the new method aims to align the largest number of planes. Using partial point clouds of multiple real-world scenes, we demonstrate the superiority of our method compared to raw 4PCS in terms of quality of result (QoS) and execution time. Our method requires about half the execution time of 4PCS in all the tested datasets and produces better alignment of the point clouds.

[1]  Samarjit Chakraborty,et al.  Computing Largest Common Point Sets under Approximate Congruence , 2000, ESA.

[2]  Chi Zhou,et al.  V4PCS: Volumetric 4PCS Algorithm for Global Registration , 2017 .

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

[4]  Wen Wang,et al.  A New Method for Registration of 3D Point Sets with Low Overlapping Ratios , 2015 .

[5]  Fabio Remondino,et al.  A REVIEW OFPOINT CLOUDS SEGMENTATION AND CLASSIFICATION ALGORITHMS , 2017 .

[6]  M. Hebert,et al.  The Representation, Recognition, and Locating of 3-D Objects , 1986 .

[7]  Paul Checchin,et al.  CICP: Cluster Iterative Closest Point for sparse-dense point cloud registration , 2018, Robotics Auton. Syst..

[8]  Nico Blodow,et al.  Aligning point cloud views using persistent feature histograms , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[10]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using unit quaternions , 1987 .

[11]  Peter Biber,et al.  The normal distributions transform: a new approach to laser scan matching , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[12]  Howie Choset,et al.  Registration with a small number of sparse measurements , 2019, Int. J. Robotics Res..

[13]  Wolfgang Förstner,et al.  Efficient and Accurate Registration of Point Clouds with Plane to Plane Correspondences , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[14]  Javier González,et al.  Scene structure registration for localization and mapping , 2016, Robotics Auton. Syst..

[15]  Tom Duckett,et al.  Scan registration for autonomous mining vehicles using 3D‐NDT , 2007, J. Field Robotics.

[16]  Bin Liang,et al.  An Iterative Closest Points Algorithm for Registration of 3D Laser Scanner Point Clouds with Geometric Features , 2017, Sensors.

[17]  Nico Blodow,et al.  Fast Point Feature Histograms (FPFH) for 3D registration , 2009, 2009 IEEE International Conference on Robotics and Automation.

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

[19]  Anh Nguyen,et al.  3D point cloud segmentation: A survey , 2013, 2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM).

[20]  Manuel Menezes de Oliveira Neto,et al.  Real-time detection of planar regions in unorganized point clouds , 2015, Pattern Recognit..

[21]  L. Dagum,et al.  OpenMP: an industry standard API for shared-memory programming , 1998 .

[22]  Higinio González-Jorge,et al.  Automatic classification of urban ground elements from mobile laser scanning data , 2018 .

[23]  Sven Behnke,et al.  Registration with the Point Cloud Library: A Modular Framework for Aligning in 3-D , 2015, IEEE Robotics & Automation Magazine.