An experimental assessment of the HSM3D algorithm for sparse and colored data

We recently introduced HSM3D, an algorithm to solve the six dimensional scan-matching problem without relying on features in the input, and whose solution does not depend on initial guesses. Building upon these new findings, in this manuscript we present a more detailed experimental study of the algorithm we proposed. In particular, we show how to improve the algorithm's performance also when matching point clouds produced by stereo cameras, given that this kind of input invalidates some of the assumptions we formerly identified in order to accelerate HSM3D's performance. We also show that by incorporating color information into the the algorithm it is possible to reduce the number of sporadic outliers in the solution set, thus providing a more reliable algorithm.

[1]  Evangelos E. Milios,et al.  Globally Consistent Range Scan Alignment for Environment Mapping , 1997, Auton. Robots.

[2]  Joachim Hertzberg,et al.  3D mapping the Kvarntorp mine : a rield experiment for evaluation of 3D scan matching algorithms , 2008 .

[3]  Wolfgang Straßer,et al.  Registration of colored 3D point clouds with a Kernel-based extension to the normal distributions transform , 2008, 2008 IEEE International Conference on Robotics and Automation.

[4]  Guannan Gao,et al.  Probabilistic Hough Transform , 2011 .

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

[6]  James R. Bergen,et al.  Visual odometry , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[7]  Luca Iocchi,et al.  Scan Matching in the Hough Domain , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[8]  Tom Duckett,et al.  Scan registration for autonomous mining vehicles using 3D-NDT: Research Articles , 2007 .

[9]  Clark F. Olson,et al.  Robust stereo ego-motion for long distance navigation , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[10]  Kostas Daniilidis,et al.  Fully Automatic Registration of 3D Point Clouds , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[11]  Stefano Carpin,et al.  HSM3D: Feature-less global 6DOF scan-matching in the Hough/Radon domain , 2009, 2009 IEEE International Conference on Robotics and Automation.

[12]  Stefano Carpin.  Merging maps via Hough transform , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Kurt Konolige,et al.  Real-time Localization in Outdoor Environments using Stereo Vision and Inexpensive GPS , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[14]  K. Gorski,et al.  HEALPix: A Framework for High-Resolution Discretization and Fast Analysis of Data Distributed on the Sphere , 2004, astro-ph/0409513.

[15]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

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

[17]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.