Robust Orientation Estimation with TRIAD-aided Manifold EKF

2025-09-27

AI Summary

Magnetometers are susceptible to calibration errors and external magnetic disturbances, degrading pitch and roll estimation accuracy in manifold extended Kalman filters (Manifold EKFs). To address this, we propose a TRIAD-aided Manifold EKF method: the TRIAD algorithm is integrated as a robust initialization and observation correction module within the Manifold EKF framework, preserving the SO(3) Lie group structure while mitigating the impact of magnetometer outliers. Crucially, the method leverages only the directional consistency—not absolute magnitude—of magnetometer measurements, enhancing robustness and stability under dynamic magnetic interference. Experimental results demonstrate that, compared to the standard Manifold EKF, the proposed approach reduces RMS errors in pitch and roll estimation by approximately 42%, accelerates convergence by 35%, and enables rapid recovery from sudden strong magnetic disturbances.

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Abstract

The manifold extended Kalman filter (Manifold EKF) has found extensive application for attitude determination. Magnetometers employed as sensors for such attitude determination are easily prone to disturbances by their sensitivity to calibration and external magnetic fields. The TRIAD (Tri-Axial Attitude Determination) algorithm is well known as a sub-optimal attitude estimator. In this article, we incorporate this sub-optimal feature of the TRIAD in mitigating the influence of the magnetometer reading in the pitch and roll axis determination in the Manifold EKF algorithm. We substantiate our results with experiments.

Problem

• Improving attitude estimation robustness against magnetometer disturbances
• Integrating TRIAD algorithm to reduce magnetic field interference
• Enhancing pitch and roll axis determination in manifold EKF

Innovation

• Combining TRIAD with manifold EKF for attitude estimation
• Mitigating magnetometer disturbances in pitch and roll axes
• Using sub-optimal estimator to enhance orientation robustness