  Simultaneous localization and mapping (SLAM) has been extensively researched
in past years particularly with regard to range-based or visual-based sensors.
Instead of deploying dedicated devices that use visual features, it is more
pragmatic to exploit the radio features to achieve this task, due to their
ubiquitous nature and the widespread deployment of Wi-Fi wireless network. This
paper presents a novel approach for collaborative simultaneous localization and
radio fingerprint mapping (C-SLAM-RF) in large unknown indoor environments. The
proposed system uses received signal strengths (RSS) from Wi-Fi access points
(AP) in the existing infrastructure and pedestrian dead reckoning (PDR) from a
smart phone, without a prior knowledge about map or distribution of AP in the
environment. We claim a loop closure based on the similarity of the two radio
fingerprints. To further improve the performance, we incorporate the turning
motion and assign a small uncertainty value to a loop closure if a matched
turning is identified. The experiment was done in an area of 130 meters by 70
meters and the results show that our proposed system is capable of estimating
the tracks of four users with an accuracy of 0.6 meters with Tango-based PDR
and 4.76 meters with a step counter-based PDR.
