High-accuracy Indoor Positioning Technology for the Large-scale Hospital Applications

Location tracking in emergency responses and environmental surveys of indoor scenarios tend to rely only on their own mobile devices, reducing the usage of external services. Low-cost and small-sized inertial measurement units (IMU) have been widely distributed in mobile devices. However, they suffer from high-level noises, leading to drift in position estimation over time. In this work, we present a graph-based indoor 3D pedestrian location tracking with inertial-only perception. Experiments in different scenarios are performed using a smartphone to evaluate the performance of the proposed method, which can achieve better positioning solutions than current learning-based and filtering-based methods, leading to an accuracy of meter level. Moreover, the proposed method is also discussed in different aspects, including the accuracy of offline optimization and proposed height regression, and the reliability of the multi-hypothesis behavior loop closures.