Asynchronous, electromagnetic sensor fusion in RatSLAM

We show the predictive value of the mean mutual information rate about the RatSLAM algorithm for electromagnetic sensors and their combinations. We calculated the mean mutual information between positions in the environment and sensor measurements performed at a specific position in the environment and defined the mean mutual information rate depending on the sensor´s measurement rate. We compare these results to RatSLAM experience maps generated using these sensors and sensor combinations and define a mean error to quantify the spatial quality of the experience map. We conclude that the mean mutual information rate generally predicts the performance correctly, but also find and explain discrepancies in specific cases.