Quaternion-Based Kalman Filter With Vector Selection for Accurate Orientation Tracking

Abstract

Human body orientation estimation from microinertial/magnetic sensor units is highly important for synthetic environments, robotics, and other human-computer interaction applications. In practice, the main challenge is how to deal with linear acceleration interference and magnetic disturbance which always cause significant attitude-estimation errors. In this paper, we present a novel quaternion-based Kalman filter with vector selection scheme for accurate human body orientation estimation using an inertial/magnetic sensor unit. In the proposed algorithm, the gyroscope measurement is used as an input to construct the linear process equation, and the accelerometer and magnetometer measurements are manipulated to establish the linear pseudomeasurement equation. A linear Kalman filter is then deployed to estimate the body orientation. In the Kalman filter framework, a vector selection scheme is designed to protect the algorithm against undesirable conditions such as temporary intensive movement and magnetic disturbance and enable it to acquire more accurate orientation estimation. The experimental results have shown that the proposed algorithm can provide accurate attitude estimations with regard to the ground truth.