Adaptive Error-State Kalman Filter for Attitude Determination on a Moving Platform

Abstract

This paper presents a loosely-coupled visual/inertial hybrid system for relative attitude determination on a moving platform. The inertial measurements are accomplished by two gyroscopes, of which the slave gyroscope removes redundant motion of the platform from the total motion sensed by the master gyroscope. To correct integration drift error and compensate for the gyroscope biases, we fuse the inertial and visual measurements fused together through the error-state Kalman filter (ESKF). Furthermore, an improved ESKF is proposed by utilizing the variational Bayesian (VB) inference to adaptively estimate the process and measurement noise covariance. To realize direct estimation of the process noise covariance, a latent variable is introduced, and inferred together with the noise covariances and error-state. The practical experimental setup is built up and experiments of two different motion trajectories are conducted. The ESKF, the cubature Kalman filter (CKF), the extended Kalman filter (EKF), the proposed filter and the other two adaptive filters are implemented for comparison to verify the effectiveness of the proposed filter. The results demonstrate the proposed filter show superiority in reducing both the root mean square error (RMSE) and maximum error (ME), and improving the filter consistency.