  Inference and decision making under uncertainty are key processes in every
autonomous system and numerous robotic problems. In recent years, the
similarities between inference and decision making triggered much work, from
developing unified computational frameworks to pondering about the duality
between the two. In spite of these efforts, inference and control, as well as
inference and belief space planning (BSP) are still treated as two separate
processes. In this paper we propose a paradigm shift, a novel approach which
deviates from conventional Bayesian inference and utilizes the similarities
between inference and BSP. We make the key observation that inference can be
efficiently updated using predictions made during the decision making stage,
even in light of inconsistent data association between the two. We developed a
two staged process that implements our novel approach and updates inference
using calculations from the precursory planning phase. Using autonomous
navigation in an unknown environment along with iSAM2 efficient methodologies
as a test case, we benchmarked our novel approach against standard Bayesian
inference, both with synthetic and real-world data (KITTI dataset). Results
indicate that not only our approach improves running time by at least a factor
of two while providing the same estimation accuracy, but it also alleviates the
computational burden of state dimensionality and loop closures.
