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This paper formulates active SLAM as a partially observable Markov decision process (POMDP) to derive near-optimal solutions for robot exploration and mapping. They introduce a novel exploration stage cost that incorporates state geometry to guide information-gathering actions. Through rigorous analysis and regularity condition studies, they justify approximate solutions and demonstrate near-optimal policy learning using standard algorithms in a specific case study.
Optimal robot exploration can be achieved by framing SLAM as a POMDP with a geometry-aware exploration cost, enabling near-optimal policy learning.
Simultaneous localization and mapping (SLAM) is a foundational state estimation problem in robotics in which a robot accurately constructs a map of its environment while also localizing itself within this construction. We study the active SLAM problem through the lens of optimal stochastic control, thereby recasting it as a decision-making problem under partial information. After reviewing several commonly studied models, we present a general stochastic control formulation of active SLAM together with a rigorous treatment of motion, sensing, and map representation. We introduce a new exploration stage cost that encodes the geometry of the state when evaluating information-gathering actions. This formulation, constructed as a nonstandard partially observable Markov decision process (POMDP), is then analyzed to derive rigorously justified approximate solutions that are near-optimal. To enable this analysis, the associated regularity conditions are studied under general assumptions that apply to a wide range of robotics applications. For a particular case, we conduct an extensive numerical study in which standard learning algorithms are used to learn near-optimal policies.
