Uncertain Spatial Knowledge Management in a Mobile Robot Architecture

In this paper a new approach to spatial knowledge management for a mobile robot is proposed. The framework merges two types of maps, grid and feature based, and two uncertainty representations, probabilistic and fuzzy. It is implemented by a set of logical sensors, that extract features from range sensors data, filter out artifacts, and estimate the robot displacement. Indoor simultaneous localization and mapping is considered as the target application

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