involve-MI: Informative Planning with High-Dimensional Non-Parametric Beliefs

TL;DR

This paper introduces a novel method for efficient information gathering in high-dimensional, non-parametric belief spaces by leveraging low-dimensional subsets, extending mutual information estimation techniques, and applying them to active SLAM tasks.

Contribution

It extends mutual information estimation to general distributions and high-dimensional settings, enabling more efficient informative planning without loss of accuracy.

Findings

Improved accuracy in active SLAM simulations.

Enhanced computational efficiency in high-dimensional belief spaces.

Effective mutual information estimation without belief surface reconstruction.

Abstract

One of the most complex tasks of decision making and planning is to gather information. This task becomes even more complex when the state is high-dimensional and its belief cannot be expressed with a parametric distribution. Although the state is high-dimensional, in many problems only a small fraction of it might be involved in transitioning the state and generating observations. We exploit this fact to calculate an information-theoretic expected reward, mutual information (MI), over a much lower-dimensional subset of the state, to improve efficiency and without sacrificing accuracy. A similar approach was used in previous works, yet specifically for Gaussian distributions, and we here extend it for general distributions. Moreover, we apply the dimensionality reduction for cases in which the new states are augmented to the previous, yet again without sacrificing accuracy. We then continue by developing an estimator for the MI which works in a Sequential Monte Carlo (SMC) manner, and avoids the reconstruction of future belief's surfaces. Finally, we show how this work is applied to the informative planning optimization problem. This work is then evaluated in a simulation of an active SLAM problem, where the improvement in both accuracy and timing is demonstrated.