3D laser scan registration of dual-robot system using vision

Authors:
Ravi Kaushik;Jizhong Xiao;William Morris;Zhigang Zhu
Affiliations:
Dept. of Computer Science, The Graduate Center, The City University of New York, New York, NY;Dept. of Electrical Engineering, CUNY City College and Dept. of Computer Science, CUNY Graduate Center,New York, NY;Dept. of Electrical Engineering, CUNY City College, New York, NY;Dept. of Computer Science, CUNY City College and CUNY Graduate Center, New York, NY
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

Citing 5
Cited 1

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
Linear N-Point Camera Pose Determination

IEEE Transactions on Pattern Analysis and Machine Intelligence
Monte Carlo localization: efficient position estimation for mobile robots

AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
Morphological iterative closest point algorithm

IEEE Transactions on Image Processing

Self-calibration for a 3D laser

International Journal of Robotics Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel technique to register a set of two 3D laser scans obtained from a ground robot and a wall-climbing robot which operates on the ceiling to construct a complete map of the indoor environment. Traditional laser scan registration methods like the Iterative Closest Point (ICP) algorithm will not converge to a global minimum without a good initial estimate of the transformation matrix. Our technique uses an overhead camera on the wall-climbing robot to keep line of sight with the ground robot and solves the Perspective Three Point (P3P) Problem to obtain the transformation matrix between the wall-climbing robot and the ground robot, which serves as a good initial estimate for the ICP algorithm to further refine the transformation matrix. We propose a novel particle filter algorithm to identify the real pose of the wall-climbing robot out of up to four possible solutions to P3P problem using Grunert's algorithm. The initial estimate ensures convergence of the ICP algorithm to a global minimum at all times. The simulation and experimental results indicate that the resulting composite laser map is accurate. In addition, the vision-based approach increases the efficiency by reducing the number of iterations of the ICP algorithm.