Filling the gap between low frequency measurements with their estimates

The use of redundant sensors brings a rich diversity of information, nevertheless fusing different sensors that run at vastly different frequencies into a proper estimate is still a challenging sensor fusion problem. Instead of using the size-varying measurements and thereby the size-varying filters during each sampling period, we propose to find a substitute of the unavailable low frequency measurements such that we can avoid using different sampling frequencies in one filter. In the gap between the sampling of two low frequency measurements, the use of these substitutes produces smoother estimates. In both the proof of concept simulation and the localization experiment performed on an indoor soccer robot, our proposed approach exhibits an improved performance compared to the size-varying Kalman filter methods.