Indoor positioning systems: Smart fusion of a variety of sensor readings

Abstract

Robust and versatile localization techniques are key to the success of the next industrial revolution. Yet, it is uncertain which combination of sensors will be the most robust and valuable. Thus, we present a versatile and reproducible measurement system incorporating a manifold number of state-of-the art sensors to compare and fuse the raw input data. It is shown that some techniques show very good results on the same scenario and data-set, but fall apart on translating to a slightly different scenario. In general we show that the vanilla approach to fuse the raw data achieves reasonable results in the generalization domain, demonstrating that radiofrequency (RF) localization techniques in combination with an inertial measurement unit (IMU) could result in a very robust and promising candidate for solving this challenging task.