Distributed Information Fusion Structure Based on Data Fusion Tree

Article Preview

Abstract:

A distributed information fusion structure based on data fusion tree is built to realize precise localization and efficient navigation for the mobile robot. The multi-class, multi-level information from robot and environment is fused using different algorithms in different levels, and make the robot have a deeper understanding to the whole environment. Experiments demonstrate that the new model proposed in the paper can improve the positioning precision of robot greatly, and the search efficiency and success rate are also better than traditional mode.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 225-226)

Pages:

488-491

Citation:

Online since:

April 2011

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] M. Bedworth: Probability moderation for multilevel information processing (Personal Communication, 1992).

Google Scholar

[2] L. A. Llein: Sensor and data fusion concepts and applications. SPIE Optical Engineering, 1999: 47-176.

Google Scholar

[3] M. Bedworth, J. Obrien: The Omnibus Model: A new model of data fusion, IEEE Aerospace and Electronic Systems Magazine, 2000, 15( 4) : 30-36.

DOI: 10.1109/62.839632

Google Scholar

[4] D. L. Hall, J. Llinas: An introduction to multisensor data fusion. Proc of the IEEE, 1997, 85( 1) : 6-23.

Google Scholar

[5] A. N. Steinberg: Data fusion system engineering. IEEE AESS System Magazine, 2001, (6): 7-14.

Google Scholar

[6] T. Kim, J. Shin, and S. Tak, Cell planning for indoor object tracking based on RFID, International Conference on Mobile Data Management: Systems, Services and Middleware (MDM '09), pp.709-713, Taipei, Taiwan, 18-20 May 2009.

DOI: 10.1109/mdm.2009.121

Google Scholar

[7] T. Perala, R. Piche: Robust Extended Kalman Filtering in Hybrid Positioning Applications. 4th Workshop on Positioning, Navigation and Communication (WPNC'07),2007, Page(s): 55 - 63

DOI: 10.1109/wpnc.2007.353613

Google Scholar

[8] N. Bouaynaya, D. Schonfeld: On the Optimality of Motion-Based Particle Filtering, IEEE Transactions on Circuits and Systems for Video Technology, Volume: 19 , Issue: 7: 1068 - 1072 , (2009)

DOI: 10.1109/tcsvt.2009.2020477

Google Scholar

[9] R. R. Hwang, M. Huber: A particle filter approach for multi-target tracking, IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS 2007). 2007 , 2753-2760

DOI: 10.1109/iros.2007.4399632

Google Scholar