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High accuracy localisation technologies exist but are prohibitively expensive to deploy for large indoor spaces such as warehouses, factories, and supermarkets to track assets and people. However, these technologies can be used to lend their highly accurate localisation capabilities to low-cost, commodity, and less-accurate technologies. In this paper, we bridge this link by proposing a technology-agnostic calibration framework based on artificial intelligence to assist such low-cost technologies through highly accurate localisation systems. A single-layer neural network is used to calibrate a less accurate technology using a more accurate one such as Bluetooth Low Energy (BLE) using Ultra-Wideband (UWB) and UWB using a professional motion tracking system. On a real indoor testbed, we demonstrate an improvement in accuracy of approximately 70% for BLE and 50% for UWB. Not only does the proposed approach require a very short measurement campaign, the low complexity of a single-layer neural network makes it ideal for deployment on constrained devices typically used for localisation purposes.

Standing on the Shoulders of Giants: AI-Driven Calibration of Localisation Technologies