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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(2): 101-105 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.491

Hardware and Software Co-design for the EKF Applied to the Mobile Robotics Localization Problem

Luis Contreras, Sérgio Cruz, J. M. S. T. Motta, and Carlos H. Llanos
Abstract—This paper describes a Hardware/Software Co-design approach for the Extended Kalman Filter (EKF) applied to the localization problem in mobile robotics. The EKF algorithm has been implemented and run on an Altera Cyclone IV FPGA with a Nios II embedded processor jointly with specific hardware modules, being adapted and applied to the mobile platform Pioneer 3AT (P3AT). In order to achieve this, we developed both the model of the mobile robot and its measurement systems previously to obtain the respective EKF equations. In the prediction step of the EKF algorithm, a system model based on concepts of dead-reckoning has been used and its implementation was achieved in software, using the Nios II processor. Conversely, in the estimation step of the EKF algorithm the respective equations have been implemented directly in hardware, producing an overall balanced implementation.

Index Terms—Dead-reckoning, extended Kalman filter, FPGA, hardware/software co-design, Pioneer 3AT.

L. Contreras, S. Cruz, C. H. Llanos, and J. M. S. T. Motta are with the Graduate Program of Mechatronics Systems, Department of Mechanical Engineering, University of Brasilia – UnB, Brasilia, Brazil (e-mail: lcontreras@aluno.unb.br, sergio@unb.br, jmmotta@unb.br, and llanos@unb.br).

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Cite: Luis Contreras, Sérgio Cruz, J. M. S. T. Motta, and Carlos H. Llanos, "Hardware and Software Co-design for the EKF Applied to the Mobile Robotics Localization Problem," International Journal of Machine Learning and Computing vol. 5, no. 2, pp. 101-105, 2015.

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