H4LO: Automation platform for efficient RF fingerprinting using SLAM-derived map and poses
© 2020 The Institution of Engineering and Technology. One of the main shortcomings of received signal strength-based indoor localisation techniques is the labour and timecost involved in acquiring labelled 'ground-truth' training data. This training data is often obtained through fingerprinting, whichinvolves visiting all prescribed locations to capture sensor observations throughout the environment. In this work, the authorspresent a helmet for localisation optimisation (H4LO): a low-cost robotic system designed to cut down on said labour by utilisingan off-the-shelf light detection and ranging device. This system allows for simultaneous localisation and mapping, providing thehuman user with accurate pose estimation and a corresponding map of the environment. The high-resolution location estimationcan then be used to train a positioning model, where received signal strength data is acquired from a human-worn wearabledevice. The method is evaluated using live measurements, recorded within a residential property. They compare the groundtruthlocation 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 theneed for time-consuming labour associated with registering the user's location.
IET Radar, Sonar and Navigation
Kozlowski, M., Twomey, N., Byrne, D., Pope, J., Santos-Rodríguez, R., & Piechocki, R. (2020). H4LO: Automation platform for efficient RF fingerprinting using SLAM-derived map and poses. IET Radar, Sonar and Navigation. Retrieved from https://ir.una.edu/csis_facpub/41