A Decoupled Orientation Estimation Approach for Robust Roll and Pitch Measurements in Magnetically Disturbed Environment

Abstract

Orientation estimation using inertial and magnetic sensors has permeated into various applications. However, robust roll and pitch estimates are still challenging for the orientation estimators when the sensors are exposed to magnetic disturbances. In this article, we proposed a decoupled orientation estimation approach (DOEA) to separate and achieve accurate roll and pitch estimates from the magnetic measurements. In the proposed DOEA, we formulate a reference vector, which is perpendicular to the gravitational vector and keeps constant size and direction, and derive its covariances to replace the magnetic field vector as the observation references. The sensor measurements of the two orthogonal homogeneous fields are further fused in the extended Kalman filter frame to correct the predicted orientation of the gyroscope integration. To validate the performance of the proposed DOEA, we compare it with three state-of-the-art approaches in four different experiments. In the magnetically disturbed environment without reliability validation of the sensors, experiment results show that the DOEA could provide robust and accurate roll and pitch measurements with 0.001- and 0.007-rad root-mean-square error (RMSE). The influence of the magnetic disturbances on the DOEA is controlled only on the yaw direction. In the wide-range static and dynamic mixed motion tracking of the robot, experiment results show that the proposed DOEA achieves 0.035-, 0.024-, and 0.018-rad RMSE of roll, pitch, and yaw estimates without singularities. Moreover, the proposed DOEA converges faster than the popular optimization filters and achieves computational efficiency 56% faster than the two-step orientation estimator, which is beneficial for realization on the embedded system.