CRMI: Confidence-Rich Mutual Information for Information-Theoretic Mapping

Abstract

This letter focuses on information-theoretic active mapping and exploration with beam-based range sensing robots. Traditional works based on hand-engineered inverse sensor model (ISM) mapping or kernel inference methods lead to imbalanced accuracy and efficiency. This motivates us to propose a new approach to compute mutual information more accurately, based on the continuous belief distribution over the occupancy map and called confidence-rich mutual information (CRMI). Specifically, we explicitly model the measurement dependencies between grid cells within the same measurement cone at each time step and derive the CRMI for each cell on all beams by introducing a more general beam-based sensor cause model (SCM), rather than the customized ISM. The time efficiency for CRMI mapping allows for online implementation as well. Extensive simulations and experiments show the desired exploratory behavior to unexplored and obscured regions for CRMI-based robot controllers in the unstructured and cluttered scene, even in large scale environment.