Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Journal of VLSI Signal Processing Systems - special issue on CORDIC
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
On Random Numbers And The Performance Of Genetic Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Embedded Robotics
TestU01: A C library for empirical testing of random number generators
ACM Transactions on Mathematical Software (TOMS)
Towards an Embedded Visuo-Inertial Smart Sensor
International Journal of Robotics Research
FPGA-optimised high-quality uniform random number generators
Proceedings of the 16th international ACM/SIGDA symposium on Field programmable gate arrays
A hardware framework for the fast generation of multiple long-period random number streams
Proceedings of the 16th international ACM/SIGDA symposium on Field programmable gate arrays
VHDL vs. Bluespec system verilog: a case study on a Java embedded architecture
Proceedings of the 2008 ACM symposium on Applied computing
Searching for resource-efficient programs: low-power pseudorandom number generators
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
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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.