WiFi is one of the primary tools for mobile indoor positioning due to its vast infrastructure presence. WiFi Fine Time Measurement (FTM) [1], is a WiFi protocol that enables the time of flight (ToF) of a WiFi signal to be determined. The system that applies this protocol is commonly referred to as WiFi Round Trip Timing (RTT). This enables more accurate and reliable indoor positioning than WiFi Residual Signal Strength Indicator (RSSI)-based positioning. A core problem of indoor positioning is prior knowledge of the environment. With current WiFi-based positioning a large database of landmark/access point (AP) positions and/or RSSI fingerprints is required. This paper provides a solution to this problem by harnessing a simultaneous localisation and mapping (SLAM) algorithm [2], using WiFi RTT, and pedestrian dead reckoning, which uses the inertial sensors in the smartphone . This incorporates prior work from [3] that developed filters and outlier detection tailored to WiFi RTT-based positioning and builds upon previous research of WiFi-RTT SLAM from Gentner et al. [4].

The research in this paper demonstrates WiFi-RTT SLAM across 8 different scenarios. The algorithms have no prior knowledge of the environment, other than an estimate of the mobile device location and heading at the start of the scenario. For the longest trial, which was 35 steps long, shown in Figure 1, the average position Root Mean Square Error (RMSE) across all steps throughout the movement of the pedestrian was 865mm and the final positioning error was 657mm. These results are sufficient for indoor positioning as a pedestrians path can be followed and accurately positioned with sub-metre accuracy. Figure 2 shows the positioning RMSE per step for the same trial. The maximum positioning error is 2823mm. The results of this paper show that unmapped indoor positioning using WiFi RTT is feasible for sub-metre indoor positioning.