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

Authors:
Stefano Carpin;Andrea Censi
Affiliations:
School of Engineering, University of California, Merced, CA;Control & Dynamical Systems Department, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

Citing 9
Cited 0

A probabilistic Hough transform

Pattern Recognition
A Method for Registration of 3-D Shapes

IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
Use of the Hough transformation to detect lines and curves in pictures

Communications of the ACM
Globally Consistent Range Scan Alignment for Environment Mapping

Autonomous Robots
Fully Automatic Registration of 3D Point Clouds

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Real-time Localization in Outdoor Environments using Stereo Vision and Inexpensive GPS

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Scan registration for autonomous mining vehicles using 3D-NDT: Research Articles

Journal of Field Robotics - Special Issue on Mining Robotics
HSM3D: feature-less global 6DOF scan-matching in the Hough/Radon domain

ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.