In this paper, we propose the pre-processor based on
the Evidence Supporting Measure of Similarity (ESMS) filter and also
propose the unified fusion approach (UFA) based on the general
fusion machine coupled with ESMS filter, which improve the
correctness and precision of information fusion in any fields of
application. Here we mainly apply the new approach to Simultaneous
Localization And Mapping (SLAM) of Pioneer II mobile robots. A
simulation experiment was performed, where an autonomous virtual
mobile robot with sonar sensors evolves in a virtual world map with
obstacles. By comparing the result of building map according to the
general fusion machine (here DSmT-based fusing machine and
PCR5-based conflict redistributor considereded) coupling with ESMS
filter and without ESMS filter, it shows the benefit of the selection of
the sources as a prerequisite for improvement of the information
fusion, and also testifies the superiority of the UFA in dealing with
SLAM.
[1] U. Frese, P. Larsson, T. Duckett (2005). A Multilevel Relaxation
Algorithm for Simultaneous Localization And Mapping. IEEE Trans.
Robotics and Automation, 21(2), 196-207.
[2] Shafer G. A. (1976). Mathematical Theory of Evidence. Princeton
University Press, Princeton, NJ, 297pp.
[3] Ph. Smets (2000). Data Fusion in the Transferable Belief Model,
Presented at the 2000 Int. Conf. Information Fusion, Paris.
[4] Smarandache F., Dezert J. (Editors)( 2004), Advances and Applications
of DSmT for Information Fusion, American Research Press, Rehoboth, ,
Available: http://www.gallup.unm.edu/~smarandache/DSmT-book1.pdf
[5] F. Smarandache (2004) Unification of Fusion Theories (UFT).
International Journal of Applied Mathematics & Statistics, vol. 2,1-14.
[6] R.R.Yager(1983). Hedging in the Combination of Evidence. Journal of
Information and Optimization Science, 4(1), 73~81.
[7] D. Dubois, H. Prade (1988). Representation and Combination of
Uncertainty with Belief Functions and Possibility Measures.
Computational Intelligence, vol.4, 244~264.
[8] M. Daniel (2000). Distribution of Contradictive Belief Masses in
Combination of Belief Functions. Information, Uncertainty and Fusion,
Kluwer Academic Publishers, 431~446
[9] F. Smarandache, J. Dezert (2005). Information Fusion Based on New
Proportional Conflict Redistribution Rules. Presented at the 2005 Int.
Conf. Information Fusion, Philadelphia, PA, USA.
[10] X. Li, J. Dezert, X. Huang (2006). Selection of Sources as a Prerequesite
for Information Fusion with Application to SLAM. Proc. Int. Conf. on
Information Fusion, Florence, Italy, July 10-13.
[11] J. Dezert, F. Smarandache (2003). On the Generation of Hyper-power
Sets for the DSmT. Presented at the 2003 Int. Conf. Information Fusion,
pp.1118-1125, Cairns, Queensland, Australia,8-11 .
[12] J. Dezert, F. Smarandache (2003). Partial Ordering of Hyper-power Sets
and Matrix Representation of Belief Functions within DSmT. Presented at
the 2003 Int. Conf. Information Fusion, pp.1230-1138, Cairns,
Queensland, Australia, 8-11.
[13] B. Ristic, Ph. Smets (2006) Association of Uncertain Combat ID
Declarations. Presented at the Proc. of Cogis-06 Conf.,Paris.
[14] X. Li, X. Huang, M. Wang (2006). A Comparision of The Effect of Sonar
Grid Map Building Based on DSmT and DST. IEEE Int Conf
WCICA2006, Dalian, China, in press.
[15] X. Li, X. Huang, M. Wang (2006). Robot Map Building from Sonar
Sensors and DSmT," Inf. & Sec.: An Int. J., Bulg. Acad. of Sci., Sofia.
[16] A. Martin, C. Osswald (2006). A New Generalization of The
Proportional Conflict Redistribution Rule Stable in Terms of Decision. To
appear in "Advances and Applications of DSmT for Information Fusion",
Vol.2, July 2006, F. Smarandache and J. Dezert (Editors), in preparation.
[1] U. Frese, P. Larsson, T. Duckett (2005). A Multilevel Relaxation
Algorithm for Simultaneous Localization And Mapping. IEEE Trans.
Robotics and Automation, 21(2), 196-207.
[2] Shafer G. A. (1976). Mathematical Theory of Evidence. Princeton
University Press, Princeton, NJ, 297pp.
[3] Ph. Smets (2000). Data Fusion in the Transferable Belief Model,
Presented at the 2000 Int. Conf. Information Fusion, Paris.
[4] Smarandache F., Dezert J. (Editors)( 2004), Advances and Applications
of DSmT for Information Fusion, American Research Press, Rehoboth, ,
Available: http://www.gallup.unm.edu/~smarandache/DSmT-book1.pdf
[5] F. Smarandache (2004) Unification of Fusion Theories (UFT).
International Journal of Applied Mathematics & Statistics, vol. 2,1-14.
[6] R.R.Yager(1983). Hedging in the Combination of Evidence. Journal of
Information and Optimization Science, 4(1), 73~81.
[7] D. Dubois, H. Prade (1988). Representation and Combination of
Uncertainty with Belief Functions and Possibility Measures.
Computational Intelligence, vol.4, 244~264.
[8] M. Daniel (2000). Distribution of Contradictive Belief Masses in
Combination of Belief Functions. Information, Uncertainty and Fusion,
Kluwer Academic Publishers, 431~446
[9] F. Smarandache, J. Dezert (2005). Information Fusion Based on New
Proportional Conflict Redistribution Rules. Presented at the 2005 Int.
Conf. Information Fusion, Philadelphia, PA, USA.
[10] X. Li, J. Dezert, X. Huang (2006). Selection of Sources as a Prerequesite
for Information Fusion with Application to SLAM. Proc. Int. Conf. on
Information Fusion, Florence, Italy, July 10-13.
[11] J. Dezert, F. Smarandache (2003). On the Generation of Hyper-power
Sets for the DSmT. Presented at the 2003 Int. Conf. Information Fusion,
pp.1118-1125, Cairns, Queensland, Australia,8-11 .
[12] J. Dezert, F. Smarandache (2003). Partial Ordering of Hyper-power Sets
and Matrix Representation of Belief Functions within DSmT. Presented at
the 2003 Int. Conf. Information Fusion, pp.1230-1138, Cairns,
Queensland, Australia, 8-11.
[13] B. Ristic, Ph. Smets (2006) Association of Uncertain Combat ID
Declarations. Presented at the Proc. of Cogis-06 Conf.,Paris.
[14] X. Li, X. Huang, M. Wang (2006). A Comparision of The Effect of Sonar
Grid Map Building Based on DSmT and DST. IEEE Int Conf
WCICA2006, Dalian, China, in press.
[15] X. Li, X. Huang, M. Wang (2006). Robot Map Building from Sonar
Sensors and DSmT," Inf. & Sec.: An Int. J., Bulg. Acad. of Sci., Sofia.
[16] A. Martin, C. Osswald (2006). A New Generalization of The
Proportional Conflict Redistribution Rule Stable in Terms of Decision. To
appear in "Advances and Applications of DSmT for Information Fusion",
Vol.2, July 2006, F. Smarandache and J. Dezert (Editors), in preparation.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:54526", author = "Xinde Li and Xinhan Huang and Min Wang", title = "Unified Fusion Approach with Application to SLAM", abstract = "In this paper, we propose the pre-processor based on
the Evidence Supporting Measure of Similarity (ESMS) filter and also
propose the unified fusion approach (UFA) based on the general
fusion machine coupled with ESMS filter, which improve the
correctness and precision of information fusion in any fields of
application. Here we mainly apply the new approach to Simultaneous
Localization And Mapping (SLAM) of Pioneer II mobile robots. A
simulation experiment was performed, where an autonomous virtual
mobile robot with sonar sensors evolves in a virtual world map with
obstacles. By comparing the result of building map according to the
general fusion machine (here DSmT-based fusing machine and
PCR5-based conflict redistributor considereded) coupling with ESMS
filter and without ESMS filter, it shows the benefit of the selection of
the sources as a prerequisite for improvement of the information
fusion, and also testifies the superiority of the UFA in dealing with
SLAM.", keywords = "DSmT, ESMS filter, SLAM, UFA", volume = "2", number = "4", pages = "444-8", }