Information-theoretic exploration with Bayesian optimization

We consider an autonomous exploration problem in which a mobile robot is guided by an information-based controller through an a priori unknown environment, choosing to collect its next measurement at the location estimated to be most informative within its current field of view. We propose a novel approach to predict mutual information (MI) using Bayesian optimization. Over several iterations, candidate sensing actions are suggested by Bayesian optimization and added to a committee that repeatedly trains a Gaussian process (GP). The GP estimates MI throughout the robot's action space, serving as the basis for an acquisition function used to select the next candidate. The best sensing action in the committee is executed by the robot. This approach is compared over several environments with two batch methods, one which chooses the most informative action from a set of pseudo-random samples whose MI is explicitly evaluated, and one that applies GP regression to this sample set. Our computational results demonstrate that the proposed method provides not only computational efficiency and rapid map entropy reduction, but also robustness in comparison with competing approaches.