3DIO: Low-Drift 3D Deep-Inertial Odometry for Indoor Localization Using an IMU

The use of mobile devices for indoor localization has proven to be a convenient solution for pedestrians in Internet of Things (IoT) applications. Radiofrequency (RF) signals, including Wi-Fi, Bluetooth, and others, are among the most commonly used sources. However, their availability cannot be guaranteed in all scenarios. Although pedestrian dead reckoning (PDR) using an inertial measurement unit (IMU) provides a self-contained positioning solution, it is susceptible to error accumulation due to heading uncertainties and varying motions. This paper presents a low-drift 3D deep-inertial odometry (DIO) method for indoor pedestrian localization using an IMU. The proposed approach employs a neural network to regress speeds within the human body frame, ensuring that the speeds are unaffected by absolute heading. These regressed speeds are integrated with inertial navigation to determine position. To enhance accuracy, the method incorporates an invariant extended Kalman filter (InEKF)-based integration for state estimation. Additionally, a learned height is included in the filter to improve 3D position estimation. The performance of the proposed method is validated through real-world tests in various environments. Results demonstrate that the proposed method outperforms traditional PDR, RONIN, and EKF-based techniques. Furthermore, this paper examines the method from multiple perspectives, highlighting its strengths in addressing heading drift and varying motions, as well as the impact of height constraints and behavior-based position corrections. The video (YouTube) or (BiliBili) is shared to showcase our work.