H4LO: automation platform for efficient RF fingerprinting using SLAM‐derived map and poses

Abstract

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.