MSCEKF-MIO: Magnetic-Inertial Odometry Based on Multi-State Constraint Extended Kalman Filter

TL;DR

This paper introduces MSCEKF-MIO, a magnetic-inertial odometry method that fuses magnetometer array data with inertial navigation to achieve accurate, cost-effective indoor positioning.

Contribution

It presents a novel magnetometer array-aided inertial odometry approach using a multi-state constraint EKF framework for improved indoor localization.

Findings

Achieves approximately 2.5m horizontal position RMSE on 150-250m trajectories.

Attains velocity estimation accuracy of 0.07m/s in magnetic feature-rich areas.

Outperforms existing magnetic array-aided INS algorithms in accuracy and reliability.

Abstract

To overcome the limitation of existing indoor odometry technologies which often cannot simultaneously meet requirements for accuracy cost-effectiveness, and robustness-this paper proposes a novel magnetometer array-aided inertial odometry approach, MSCEKF-MIO (Multi-State Constraint Extended Kalman Filter-based Magnetic-Inertial Odometry). We construct a magnetic field model by fitting measurements from the magnetometer array and then use temporal variations in this model-extracted from continuous observations-to estimate the carrier's absolute velocity. Furthermore, we implement the MSCEKF framework to fuse observed magnetic field variations with position and attitude estimates from inertial navigation system (INS) integration, thereby enabling autonomous, high-precision indoor relative positioning. Experimental results demonstrate that the proposed algorithm achieves superior velocity estimation accuracy and horizontal positioning precision relative to state-of-the-art magnetic array-aided INS algorithms (MAINS). On datasets with trajectory lengths of 150-250m, the proposed method yields an average horizontal position RMSE of approximately 2.5m. In areas with distinctive magnetic features, the magneto-inertial odometry achieves a velocity estimation accuracy of 0.07m/s. Consequently, the proposed method offers a novel positioning solution characterized by low power consumption, cost-effectiveness, and high reliability in complex indoor environments.