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Parallel grid-based recursive Bayesian estimation using GPU for real-time autonomous navigation

Tomonari Furukawa, Benjamin Lavis, Hugh F. Durrant-Whyte
Dept. of Mech. Eng., Virginia Tech, Blacksburg, VA, USA
IEEE International Conference on Robotics and Automation (ICRA), 2010

@conference{furukawa2009parallel,

   title={Parallel grid-based recursive Bayesian estimation using GPU for real-time autonomous navigation},

   author={Furukawa, T. and Lavis, B. and Durrant-Whyte, H.F.},

   booktitle={Robotics and Automation (ICRA), 2010 IEEE International Conference on},

   pages={316–321},

   issn={1050-4729},

   year={2009},

   organization={IEEE}

}

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This paper presents the parallelization of grid-based recursive Bayesian estimation (RBE) using a graphics processing unit (GPU) for real-time control of autonomous vehicles. Although the grid-based method has been effectively used for autonomous search due to its ability to represent search space explicitly, heavy computational load has been a bottleneck for real-time application similarly to other non-Gaussian RBE techniques. The proposed RBE, which parallelizes grid-wise computations using GPU upon the analysis of mathematical operations, removes sequential processes and accelerates RBE significantly. Numerical examples have first demonstrated the validation of the proposed RBE and investigated its performance through parametric studies. The proposed RBE was then applied to the cooperative search by autonomous unmanned ground vehicles (UGVs), and its real-time capability has been demonstrated.
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