  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.
