NDLI: A Mersenne Twister Hardware Implementation for the Monte Carlo Localization Algorithm

Content Provider	Springer Nature Link
Author	Bonato, Vanderlei Mazzotti, Bru F. Fernandes, Marcio Meri Marques, Eduardo
Copyright Year	2012
Abstract	Mobile robot localization is the problem of estimating a robot position based on sensor data and a map of the environment. One of the most used methods to address this problem is based on the Monte Carlo Localization (MCL) algorithm, which is a sample based state estimation that offers some advantages over the traditional Gaussian method. This work presents an embedded system based on an FPGA (Field-Programmable Gate Array), customized to compute the complete MCL algorithm in a response time compatible with real mobile robot applications. At the core of the system is the Mersenne Twister pseudo-random number generator, used to spread random particles over the robot navigation map. Experimental results have shown that the proposed hardware architecture is able to generate 125M numbers of 32bits/sec and that for 1k features each MCL iteration takes 0.27 sec. Additionally, this paper provides some evidences about the impact caused by the choice of random number generator on the MCL algorithm convergence speed.
Starting Page	75
Ending Page	85
Page Count	11
File Format	PDF
ISSN	19398018
Journal	Journal of Signal Processing Systems
Volume Number	70
Issue Number	1
e-ISSN	19398115
Language	English
Publisher	Springer US
Publisher Date	2012-02-24
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Mersenne twister Particle filter Embedded mobile robotics FPGA Signal, Image and Speech Processing Circuits and Systems Electrical Engineering Image Processing and Computer Vision Pattern Recognition Computer Imaging, Vision, Pattern Recognition and Graphics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Signal Processing Control and Systems Engineering Information Systems Modeling and Simulation Hardware and Architecture

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