A MODIFIED SAMPLING METHOD FOR LOCALIZATION ACCURACY IMPROVEMENT OF MONTE CARLO LOCALIZATION

Document Type : Original Article

Authors

1 Graduate student, Dept. of Mechatronics, Faculty of Engineering, Ain Shams University, Cairo, Egypt.

2 Assistant professor, Dept. of Automotive, Faculty of Engineering, Ain Shams University, Cairo, Egypt.

3 Professor, MTI University, Cairo, Egypt.

4 Professor, Dept. of Mechatronics, Faculty of Engineering, Ain Shams University, Cairo, Egypt.

Abstract

ABSTRACT
Unmanned vehicles are devices that can move around and perform tasks without an
operator onboard. Such features are essential in many applications. Localization is a
very important task in any autonomous mobile robot; in order to reliably navigate, the
robot must keep accurate track of where it is. In the past few years Monte Carlo
Localization (MCL) has been one of the most successful and popular approaches to
solve the localization problem. MCL is a Bayesian algorithm based on particle filters.
This paper is an attempt to increase the accuracy of localizing a mobile robot by
modifying the way of generating samples from the proposal distribution of the MCL
algorithm. Results show improvements in localization accuracy as compared to the
basic MCL algorithm.

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