Robot localization from minimalist inertial data using a Hidden Markov Model (original) (raw)

2014 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), 2014

Abstract

Hidden Markov Models (HMMs) are applied to interoceptive data (in this case the sense of rotation by way of a gyroscope) acquired by a moving wheeled robot when contouring an indoor environment. We demonstrate the soundness of HMMs to solve the problem of robot localization in a topological model of the environment, particularly the kidnapped robot problem and position tracking. In this approach, the environment topology is described by the sequence of movements a robot executes when contouring the environment. Movements are described in a fuzzy domain using distance traveled and curvature as features.

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