Towards efficient inference update through planning via JIP — Joint inference and belief space planning

Abstract

Inference and decision making under uncertainty are essential in numerous robotics problems. In recent years, the similarities between inference and control triggered much work, from developing unified computational frameworks to pondering about the duality between the two. In spite of the aforementioned 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 novel approach that utilizes the similarities between inference and BSP and make the key observation that inference can be efficiently updated using the precursory planning stage, thus paving the way towards a joint inference and BSP paradigm. We develop four different methods that implement our novel approach under simplifying assumptions and validate them in the context of autonomous navigation in unknown environment. Results indicate that not only our methods improve running time by at least two orders of magnitude, compared to iSAM2 paradigm, they also found to be less sensitive to state dimensionality and loop closures.