Inertial Velocity Estimation for Indoor Navigation Through Magnetic Gradient-based EKF and LSTM Learning Model

Makia Zmitri (1, 2) , Hassen Fourati (1) , Christophe Prieur (2)

1 NECS-POST - Systèmes Commandés en Réseau
2 GIPSA-INFINITY - GIPSA - Infinite Dimensional Dynamics

This paper presents a novel method to improve the inertial velocity estimation of a mobile body, for indoor navigation, using solely raw data from a triad of inertial sensors (accelerometer and gyroscope), as well as a determined arrangement of magnetometers array. The key idea of the method is the use of deep neural networks to dynamically tune the measurement covariance matrix of an Extended Kalman Filter (EKF). To do so, a Long Short-Term Memory (LSTM) model is derived to determine a pseudo-measurement of inertial velocity of the target under investigation. This measurement is used afterwords to dynamically adapt the measurement noise parameters of a magnetic field gradient-based EKF. As it was shown in the literature, there is a strong relation between inertial velocity and magnetic field gradient, which is highlighted with the proposed approach in this paper. Its performance is tested on the Openshoe dataset, and the obtained results compete with the INS/ZUPT approach, that unlike the proposed solution, can only be applied on foot-mounted applications and is not adequate to all walking paces.

Mots clés: Robot sensing systems; Magnetic fields; Noise measurement; Adaptation models; Magnetic field measurement; Magnetometers; Estimation

Domaines: Automatique / Robotique

Makia Zmitri, Hassen Fourati, Christophe Prieur. Inertial Velocity Estimation for Indoor Navigation Through Magnetic Gradient-based EKF and LSTM Learning Model. IROS 2020 - IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct 2020, Las Vegas, United States. pp.1-6, ⟨10.1109/IROS45743.2020.9340772⟩. ⟨hal-02958106⟩