Low-Cost Urban Localization with Magnetometer and LoRa Technology

With the goal of developing low-cost and innovative perception and localization techniques for autonomous vehicles, this work explores a system that solely relies on a LoRa receiver and a magnetometer for agent localization within urban environments. Using the received signal strength from LoRa beacons distributed across a test area of 16,000 square meters, a model of expected RSSI values per beacon is estimated using Gaussian Process (GP) regression. Motion is estimated using a probabilistic signal similarity classifier, and localization is obtained via a particle filter. Our experiments demonstrate that our proposed system is able to estimate our location to within three meters RMSE when the agent is within the convex hull of prior data. In real-world scenarios, characterized by signal interference and environmental complexities, our approach highlights the potential of leveraging affordable technology such as LoRa receivers and magnetometers for robust and accurate location estimation in complex urban environments. The integration of low-cost LoRa devices, Gaussian Process regression, particle filtering and our novel signal similarity motion estimator offers a promising avenue for achieving cost-effective localization solutions without compromising accuracy or reliability.