ARAS: Ambiguity-aware Robust Active SLAM based on Multi-hypothesis State and Map Estimations

Abstract

In this paper, we introduce an ambiguity-aware robust active SLAM (ARAS) framework that makes use of multi-hypothesis state and map estimations to achieve better robustness. Ambiguous measurements can result in multiple probable solutions in a multi-hypothesis SLAM (MH-SLAM) system if they are temporarily unsolvable (due to insufficient information), our ARAS aims at taking all these probable estimations into account explicitly for decision making and planning, which, to the best of our knowledge, has not yet been covered by any previous active SLAM approach (which mostly consider a single hypothesis at a time). This novel ARAS framework 1) adopts local contours for efficient multi-hypothesis exploration, 2) incorporates an active loop closing module that revisits mapped areas to acquire information for hypotheses pruning to maintain the overall computational efficiency, and 3) demonstrates how to use the output target pose for path planning under the multi-hypothesis estimations. Through extensive simulations and a real-world experiment, we demonstrate that the proposed ARAS algorithm can actively map general indoor environments more robustly than a similar single-hypothesis approach in the presence of ambiguities.