Delft University of Technology
The particle filter is a Bayesian estimation technique based on Monte Carlo simulation. It is ideal for non-linear, non-Gaussian dynamical systems with applications in many areas, such as computer vision, robotics, and econometrics. Practical use has so far been limited, because of steep computational requirements. In this paper, the authors investigate how to design a particle filter framework for complex estimation problems using many-core architectures. They develop a robotic arm application as a highly flexible estimation problem to push estimation rates and accuracy to new levels.