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.

通过结合多种最先进的传感器的数据，我们提出了一个多功能的测量系统，对其进行比较和融合，展示了单一数据融合技术在应对情境转换时的可行性和鲁棒性。特别是，在结合射频（RF）定位技术和惯性测量单元（IMU）时，这种方法在解决定位问题上具有极高的鲁棒性和可靠性。