Md. Didarul Islam; S. M. Taslim Reza; Jia Uddin; Emmanuel Oyekanlu

Laser Scan Matching by FAST CVSAC in Dynamic Environment

Full Text (PDF, 1499KB), PP.11-18

Views: 0 Downloads: 0

Author(s)

Md. Didarul Islam ^1,* S. M. Taslim Reza ² Jia Uddin ³ Emmanuel Oyekanlu ⁴

1. Department of Electrical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia

2. Department of Mechanical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia

3. Faculty of Science and Engineering, International Islamic University Chittagong, Chittagong, Bangladesh

4. Department of Electrical and Computer Engineering, Drexel University, Philadelphia, Pennsylvania, PA 19104, USA

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2013.11.02

Received: 27 Feb. 2013 / Revised: 1 May 2013 / Accepted: 20 Jul. 2013 / Published: 8 Oct. 2013

Index Terms

Scan Matching, Localization, Iterative Closest Point (ICP), Random Sample, Consensus (RANSAC) Algorithm

Abstract

Localization and mapping are very important for safe movement of robots. One possible way to assist with this functionality is to use laser scan matching. This paper describes a method to implement this functionality. It is based on well-known random sampling and consensus (RANSAC) and iterative closest point (ICP). The proposed algorithm belongs to the class of point to point scan matching approach with its matching criteria rule. The performance of the proposed algorithm is examined in real environment and found applicable in real-time application.

Cite This Paper

Md. Didarul Islam, S. M. Taslim Reza, Jia Uddin, Emmanuel Oyekanlu, "Laser Scan Matching by FAST CVSAC in Dynamic Environment", International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.11, pp.11-18, 2013. DOI:10.5815/ijisa.2013.11.02

Reference

[1]J. S. Gutmann, Robuste Navigation autonomer mobile Systeme, PhD thesis, Albert-Ludwigs-Universitat Freiburg, 2000.

[2]K. Lingemann, H. Surmann, A. Nuchter, J. Hertzberg. Indoor and outdoor localization for fast mobile robots. Proceedings - IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004, v3, pp.2185-2190.

[3]I. J. Cox. Blanche-an experiment in guidance and navigation of an autonomous robot vehicle. Proceedings - IEEE Transactions on Robotics and Automation, v7, n2, 1991, pp.193-203.

[4]P. Biber, W. Straβer. The normal distributions transform: A new approach to laser scan matching. Proceedings - IEEE/RSJ International Conference on Intelligent Robots and Systems, 2003, v3, pp.2743-2748.

[5]P. J. Besl, N. D. McKay. A method for registration of 3D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, v14, n2, 1992, pp.239-256.

[6] F. Lu, E. Milios. Robot pose estimation in unknown environments by matching 2D range scans. Journal of Intelligent and Robotic System, v20, 1997, pp.249-275.

[7]E. Golinelli, S. Musazzi, U. Perini, F. Barberis. Conductors sag monitoring by means of a laser based scanning measuring system: Experimental results. IEEE Sensors Applications Symposium, 2012, pp.1-4.

[8]M. Tomono. A scan matching method using Euclidean invariant signature for global localization and map building. Proceedings - International Conference on Robotics and Automation, 2004, pp.866-871.

[9]D. Forsyth. Computer Vision: A Modern Approach. 2nd Edition, November 2011.

[10]A. Diosi, L. Kleeman. Scan Matching in Polar Coordinates with Application to SLAM. Technical report MECSE-2005, Electrical and Computer Engineering Department, Monash University, 2005.

[11]J. Leonard, H. Durant-Wyyte. Mobile Robot Localization by Tracking Geometry Beacons. IEEE Transaction on Robotics and Automation, v7, 1991, pp.376-382.

[12]B. Jensen, R. Siegwart. Scan Alignment with Probabilistic Distance Metric. Proceedings - IEEE International Conference on Intelligent Robots and Systems, 2004, pp.2191-2196.

[13]J. Chen, E. Oyekanlu, S. Onidare, W. Kulesza. The Evaluation of the Gaussian Mixture Probability Hypothesis Density Approach for Multi-Target Tracking. Proceedings - IEEE International Conference on Imaging Systems and Techniques, 2010, pp.182-185.

[14]F. Lu, E. Milios. Globally Consistent Range Scan Alignment for Environment Mapping. Journal of Autonomous Robot, v4, n4, 1997, pp.333-349.

[15]S. Pfister, K. Kriechbaum, S. Roumeliotis, J. Burdick. Weighted Range Sensor Matching Algorithms for Mobile Robot Displacement Estimation. Proceedings - IEEE International Conference on Robotics and Automation, 2002, pp.11-15.

[16]J. Minguez, F. Lamiraux and L. Montesano. Metric-based Scan matching Algorithm for Mobile Robot Displacement Estimation. Proceedings - IEEE/RJS International Conference on Robotics and Automation, 2005, pp.3557-3563.

[17]F. Lu, E. Milios. Robot Pose Estimation in Unknown Environment by Matching 2D Range Scans. Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994, pp.935-938.

[18]A. Censi, L. Iocchi, G. Grisetti. Scan Matching in the Hough Domain. Proceedings - International Conference on Robotics and Automation, 2005, pp.2739-2744.

[19]J. Gutmann, C. Schlegel. AMOS: Comparison of Scan Matching Approaches for Self Localization in Indoor Environments. Proceedings - First Euromico Workshop on Advanced Mobile Robots, 1996, pp.61-67.

[20]R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2004.

[21]M. Zuliani. RANSAC for Dummies. http://vision.ece.ucsb.edu/~zuliani/Research/RANSAC/docs/RANSAC4Dummies.pdf

[22]H. Y. Kim, S. Lee, B. J. You. Robust Laser Scan Matching in Dynamic Environments. Proceedings - IEEE International Conference on Robotics and Biomimetic, 2009, pp.2284-2289.

International Journal of Intelligent Systems and Applications (IJISA)