Selection of sources as a prerequesite for information fusion with application to SLAM
Xinde Li, Jean Dezert, Xinhan Huang · 2006
We consider in this work evidential sources of information and propose a very general evidence supporting measure of similarity (ESMS) for selecting the most coherent subset of sources to combine among all sources available at each instant. The methodology proposed here coupled with a DSmT-based fusion machine is tested in robotics for the automatic estimation of an unknown simulated environment with obstacles where an autonomous mobile Pioneer II robot with sonar sensors evolves. Our simulation results are based on the fusion of similar and equireliable sensors but same approach can also be used with dissimilar sources as well by using a discounting method taking into account the reliability of each sensor. Our results show clearly the benefit of the selection of the sources as prerequisite for improvement of information fusion