Pose estimation by extended Kalman filter using noise covariance matrices based on sensor output

• Published in: ROBOMECH journal Vol. 7; no. 1; pp. 1 - 11
• Main Authors: Saito, Ayuko, Kizawa, Satoru, Kobayashi, Yoshikazu, Miyawaki, Kazuto
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 19.10.2020; SpringerOpen
• Subjects: Artificial Intelligence; Computational Intelligence; Control and Systems Theory; Engineering; Kalman filter; Mechatronics; Motion sensor; Noise covariance matrix; Pose estimation; Research Article; Robotics and Automation; Sensor fusion; Sensor fusion; Motion sensor; Pose estimation; Kalman filter; Noise covariance matrix
• ISSN: 2197-4225; 2197-4225
• DOI: 10.1186/s40648-020-00185-y

This paper presents an extended Kalman filter for pose estimation using noise covariance matrices based on sensor output. Compact and lightweight nine-axis motion sensors are used for motion analysis in widely various fields such as medical welfare and sports. A nine-axis motion sensor includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. Information obtained from the three sensors is useful for estimating joint angles using the Kalman filter. The extended Kalman filter is used widely for state estimation because it can estimate the status with a small computational load. However, determining the process and observation noise covariance matrices in the extended Kalman filter is complicated. The noise covariance matrices in the extended Kalman filter were found for this study based on the sensor output. Postural change appears in the gyroscope output because the rotational motion of the joints produces human movement. Therefore, the process noise covariance matrix was determined based on the gyroscope output. An observation noise covariance matrix was determined based on the accelerometer and magnetometer output because the two sensors’ outputs were used as observation values. During a laboratory experiment, the lower limb joint angles of three participants were measured using an optical 3D motion analysis system and nine-axis motion sensors while participants were walking. The lower limb joint angles estimated using the extended Kalman filter with noise covariance matrices based on sensor output were generally consistent with results obtained from the optical 3D motion analysis system. Furthermore, the lower limb joint angles were measured using nine-axis motion sensors while participants were running in place for about 100 s. The experiment results demonstrated the effectiveness of the proposed method for human pose estimation.