A Hybrid Framework for Mobile Robot Localization: Formulation Using Switching State-Space Models

In this paper we address one of the most important issues for autonomous mobile robots, namely their ability to localize themselves safely and reliably within their environments. We propose a probabilistic framework for modelling the robot's state and sensory information based on a Switching State-Space Model. The proposed framework generalizes two of the most successful probabilistic model families currently used for this purpose: the Kalman filter Linear models and the Hidden Markov Models. The proposed model combines the advantages of both models, relaxing at the same time inherent assumptions made individually in each of these existing models.