Particle filtering for state estimation in industrial robotic systems

State estimation is a major problem in industrial systems. To this end, Gaussian and non-parametric filters have been developed. In this paper the extended Kalman filter which assumes Gaussian measurement noise is compared to the particle filter which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of an industrial robot is used, when measurements are available from an accelerometer mounted on the end-effector of the robotic manipulator and from the encoders of the joint motors. It is shown that in this kind of sensor fusion problem the particle filter outperforms the extended Kalman filter, at the cost of more demanding computations.