Consistent sparsification 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, the sparsification here is done in the context of efficient decision making, with no impact on the state inference. By identifying state variables which are uninvolved in the decision, we generate a sparse version of the state's information matrix, to be used in the examination of candidate actions. This sparse approximation 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. The approach is put to the test in a SLAM simulation, where a significant improvement in runtime is achieved. Nevertheless, the method is generic, and not tied to a specific type of problem.