Multi-modal sensor data and information fusion for localization in indoor environments

The work presents the development of a framework for sensor data and complementary information fusion for localization in indoor environments. The framework is based on a modular and flexible sensor unit, which can be attached to a person and which contains various sensor types, such as range sensors, inertial and magnetic sensors or barometers. All measurements are processed within Bayesian Recursive Estimation algorithms and combined with available a priori knowledge such as map information or human motion models.