involve-MI: Informative Planning with High-Dimensional Non-Parametric Beliefs
This work addresses the problem of computational efficiency in robotic planning for tasks like SLAM, though it is incremental as it extends prior Gaussian-based methods to general distributions.
The paper tackles the challenge of efficient information gathering in high-dimensional, non-parametric belief spaces by exploiting sparsity to compute mutual information over lower-dimensional subsets without accuracy loss, and demonstrates improved accuracy and timing in an active SLAM simulation.
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.