Evaluating the Synergy of Relative and Absolute Indoor Localization in Industrial Spaces

Industrial Internet of Things (IIoT) applications benefit from the knowledge of the device and user positions in a manifold way. Reliable indoor navigation combined with IIoT enables Location-Based-Services (LBS) such as assistance functions of moveable actuators. A crane which follows its operator can significantly increase the efficiency of the process. Safety mechanisms are also enhanced by positioning information. For example, exclusion areas where only automated devices are operating can be implemented. In this paper a novel localization framework is introduced to fuse sensor data from either absolute or relative positioning sources. The core of the framework is an Extended Kalman-Filter (EKF) architecture that is able to handle data from several different sources. Each localization source needs to fulfill requirements regarding data representation and structure defined by the framework, e.g., current state and variance. The approach is verified in a real world scenario with two different sensor types as information sources: Ultra Wide Band localization and Pedestrian Dead Reckoning. We show that the combination of these technologies improves the localization accuracy and evaluate advantages and drawbacks of this approach.