Scalable sparsification for efficient decision making under uncertainty in high dimensional state spaces

In this paper we introduce a novel sparsification method for efficient decision making under uncertainty and belief space planning in high dimensional state spaces. By using a sparse version of the state's information matrix, we are able to improve the high computational cost of examination of all candidate actions. We also present an in-depth analysis for the general case of approximated decision making, and use it in order to set bounds over the induced error in potential revenue. The scalability of the method allows balancing between the degree of sparsification and the tolerance for this error, in order to maximize its benefits. The approach differs from recent methods by focusing on improving the decision making process directly, and not as a byproduct of a sparsification of the state inference. Eventually, we demonstrate the superiority of the approach in a SLAM simulation, where we manage to maintain the accuracy of the solution, while demonstrating a significant improvement in run time.