A COMPARATIVE STUDY AMONG THREE REGISTRATION ALGORITHMS: PERFORMANCE, QUALITY ASSURANCE AND ACCURACY

Abstract. A critical task in every terrestrial laser scanning project is the transformation (addressed to as registration or alignment) of multiple point clouds into a common reference system. Even though this operation appears to be a solved and well-understood problem, the vast majority of available techniques still lack meaningful quality measures that allow the user to understand and analyze the final outputs. The erroneous estimation of registration parameters may cause systematic biases that falsify those subsequently outcomes such as deformation measurements on historical buildings, CAD-drawings of individual elements, or 3D models devoted to analyze the verticality of a tower. Thus, this article compares three common registration algorithms, namely target-based registration, the Iterative-Closest Point algorithm (ICP) as well as a plane-based approach on examples related to different case studies concerning historical buildings.

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

[2]  Nai Xia,et al.  Method of Registration , 2014 .

[3]  George Vosselman,et al.  Airborne and terrestrial laser scanning , 2011, Int. J. Digit. Earth.

[4]  Marco Scaioni,et al.  Change Detection and Deformation Analysis in Point Clouds , 2013 .

[5]  Roderik Lindenbergh,et al.  Change detection and deformation analysis using static and mobile laser scanning , 2015 .

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

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

[8]  Luigi Barazzetti,et al.  Scan registration using planar features , 2014 .

[9]  Daniel Wujanz,et al.  TOWARDS TRANSPARENT QUALITY MEASURES IN SURFACE BASED REGISTRATION PROCESSES: EFFECTS OF DEFORMATION ONTO COMMERCIAL AND SCIENTIFIC IMPLEMENTATIONS , 2012 .

[10]  Darius Burschka,et al.  A correlation based target finder for terrestrial laser scanning , 2008 .

[11]  G. Sithole,et al.  Recognising structure in laser scanning point clouds , 2004 .

[12]  M. Menenti,et al.  Scanning geometry: Influencing factor on the quality of terrestrial laser scanning points , 2011 .

[13]  M. Scaioni.  On the Estimation of Rigid-Body Transformation for Tls Registration , 2012 .

[14]  L. Gründig,et al.  PLANE-BASED REGISTRATION OF SEVERAL THOUSAND LASER SCANS ON STANDARD HARDWARE , 2018, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[15]  Jacky C. K. Chow,et al.  Low Cost Artificial Planar Target Measurement Techniques for Terrestrial Laser Scanning , 2010 .

[16]  Mattia Previtali,et al.  Indoor Building Reconstruction from Occluded Point Clouds Using Graph-Cut and Ray-Tracing , 2018 .

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

[18]  M. Scaioni,et al.  APPLICATION OF TLS TO SUPPORT LANDSLIDES STUDY: SURVEY PLANNING, OPERATIONAL ISSUES AND DATA PROCESSING , 2004 .