Standing on the Shoulders of Giants: AI-driven Calibration of Localisation Technologies
This work addresses the challenge of affordable asset and people tracking in large indoor spaces like warehouses and supermarkets, though it is incremental as it builds on existing localization technologies.
The paper tackles the problem of high deployment costs for accurate indoor localization by proposing an AI-driven calibration framework that uses a single-layer neural network to enhance low-cost technologies like BLE and UWB with more accurate systems, resulting in approximately 70% accuracy increase for BLE and 50% for UWB in a real testbed.
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 less accurate technology using more accurate one such as BLE using UWB and UWB using a professional motion tracking system. On a real indoor testbed, we demonstrate an increase in accuracy of approximately 70% for BLE and 50% for UWB. Not only the proposed approach requires a very short measurement campaign, the low complexity of the single-layer neural network also makes it ideal for deployment on constrained devices typically for localisation purposes.