H4LO: Automation Platform for Efficient RF Fingerprinting using SLAM-derived Map and Poses

One of the main shortcomings of received signal strength-based indoor localisation techniques is the labour and time cost involved in acquiring labelled ‘ground-truth’ training data. This training data is often obtained through fingerprinting, which involves visiting all prescribed locations to capture sensor observations throughout the environment. In this work, the authors present a helmet for localisation optimisation (H4LO): a low-cost robotic system designed to cut down on said labour by utilising an off-the-shelf light detection and ranging device. This system allows for simultaneous localisation and mapping, providing the human user with accurate pose estimation and a corresponding map of the environment. The high-resolution location estimation can then be used to train a positioning model, where received signal strength data is acquired from a human-worn wearable device. The method is evaluated using live measurements, recorded within a residential property. They compare the groundtruth location labels generated automatically by the H4LO system with a camera-based fingerprinting technique from previous work. They find that the system remains comparable in performance to the less efficient camera-based method, whilst removing the need for time-consuming labour associated with registering the user's location.