A MODIFIED SAMPLING METHOD FOR LOCALIZATION ACCURACY IMPROVEMENT OF MONTE CARLO LOCALIZATION | ||||
| The International Conference on Applied Mechanics and Mechanical Engineering | ||||
| Article 75, Volume 18, 18th International Conference on Applied Mechanics and Mechanical Engineering., April 2018, Page 1-9 PDF (140.85 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/amme.2018.35021 | ||||
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| Authors | ||||
| M. A. Awad-Allah1; M. A. Abdelaziz2; M. A. Shahin3; F. A. Tolbah4 | ||||
| 1Graduate student, Dept. of Mechatronics, Faculty of Engineering, Ain Shams University, Cairo, Egypt. | ||||
| 2Assistant professor, Dept. of Automotive, Faculty of Engineering, Ain Shams University, Cairo, Egypt. | ||||
| 3Professor, MTI University, Cairo, Egypt. | ||||
| 4Professor, 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. | ||||
| Keywords | ||||
| Monte Carlo Localization, Mobile robots; Position estimation; Particle filters | ||||
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