A Mersenne Twister Hardware Implementation for the Monte Carlo Localization Algorithm

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.

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