Poisson-driven dirt maps for efficient robot cleaning

Being able to estimate the dirt distribution in an environment makes it possible to compute efficient paths for robotic cleaners. In this paper, we present a novel approach for modeling and estimating the dynamics of the generation of dirt in an environment. Our model uses cell-wise Poisson processes on a regular grid to estimate the distribution of dirt in the environment. It allows for an effective estimation of the dynamics of the generation of dirt and for making predictions about the absolute dirt values. We propose two efficient cleaning policies that are based on the estimated dirt distributions and can easily be adapted to different needs of potential users. Through extensive experiments carried out with a modified iRobot Roomba vacuum cleaning robot and in simulation we demonstrate the effectiveness of our approach.