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Simultaneous localization and Planning (SLAP) is a crucial ability for anautonomous robot operating under uncertainty. In its most general form, SLAPinduces a continuous POMDP (partially-observable Markov decision process),which needs to be repeatedly solved online. This paper addresses this problemand proposes a dynamic replanning scheme in belief space. The underlying POMDP,which is continuous in state, action, and observation space, is approximatedoffline via sampling-based methods, but operates in a replanning loop online toadmit local improvements to the coarse offline policy. This construct enablesthe proposed method to combat changing environments and large localizationerrors, even when the change alters the homotopy class of the optimaltrajectory. It further outperforms the state-of-the-art FIRM (Feedback-basedInformation RoadMap) method by eliminating unnecessary stabilization steps.Applying belief space planning to physical systems brings with it a plethora ofchallenges. A key focus of this paper is to implement the proposed planner on aphysical robot and show the SLAP solution performance under uncertainty, inchanging environments and in the presence of large disturbances, such as akidnapped robot situation.

SLAP: Simultaneous Localization and Planning Under Uncertainty for Physical Mobile Robots via Dynamic Replanning in Belief Space: Extended version