Human SLAM, Indoor Localisation of Devices and Users

Abstract

The indoor localisation problem is more complex than just finding whereabouts of users. Finding positions of users relative to the devices of a smart space is even more important. Unfortunately, configuring such systems manually is a tedious process, requires expert knowledge, and is sensitive to changes in the environment. Moreover, many existing solutions do not take user privacy into account. We propose a new system, called Simultaneous Localisation and Configuration (SLAC), to address the problem of locating devices and users relative to those devices, and combine this problem into a single estimation problem. The SLAC algorithm, based on FastSLAM, is able to locate devices using the received signal strength indicator (RSSI) of devices and motion data from users. Simulations have been used to show the performance in a controlled environment and the effect of the amount of RSSI updates on the localisation error. Live tests in non-trivial environments showed that we can achieve room level accuracy and that the localisation can be performed in real time. This is all done locally, i.e. running on a user's device, with respect for privacy and without using any prior information of the environment or device locations. Although promising, more work is required to increase accuracy in larger environments and to make the algorithm more robust for environment noise caused by walls and other objects. Existing techniques, e.g. map fusing, can alleviate these problems.