Automated Wireless Localization Data Acquisition and Calibration with 6DOF Image Localization


Radio frequency (RF) signals have been used extensively to enable (indoor) localization and proximity detection based on Received Signal Strength Indication (RSSI). However, localization systems often suffer from large data collection and calibration overhead, especially when being deployed in a new environment. RSSI fingerprinting based localization systems require the construction of a fingerprinting database. This localization data acquisition is a hindrance for the proliferation of localization systems in practice. Similarly, RSSI proximity applications require an RSSI calibration for the receiver hardware and the deployment environment. To overcome these problems, we propose the usage of visual 3D models which enable 6DOF localization and distance measurement with high accuracy. We then fuse this physical knowledge with RF data: (1) for automated acquisition of fingerprinting data and (2) easy calibration of a RF propagation model for proximity estimation.

Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers
Jonathan Fürst
Postdoctoral Researcher