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

In this paper we introduce a novel approach for efficient decision making under uncertainty and belief space planning, in high dimensional state spaces. While recently developed methods focus on sparsifying the inference process, we use sparsification in the context of decision making, with no impact on the inference. By identifying state variables which are uninvolved in decision making, we generate a sparse version of the state's information matrix, to be used in the examination of candidate actions. The approach is action consistent, i.e. has no influence on the action selection. Overall we manage to maintain the same quality of solution, while reducing the computational complexity of the problem. Moreover, the method is generic, with no limitations to a specific type of problem. The approach is put to the test in a SLAM simulation, where a significant improvement in runtime is achieved.