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One of the major problems in robotics is to recognize the robots position with respect to a given environment. More recently researchers have begun to exploit the structural properties of robotic domains that have led to great success. A general solution for such problem is the implementation of particle filters. The particle filter is more efficient than any other tracking algorithm because this mechanism follows Bayesian estimation rule of conditional probability propagation. In this paper we would like to present an approach to improvise the particle filter algorithm using SIFT pattern recognition technique and image database processing to obtain unimodal uncertainty for effective position tracking.

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