9축 IMU기반 자세 추정을 위한 순환 신경망: 교란조건에서의 3차원 자세 추정 성능 (Recurrent Neural Network for Nine-Axis IMU-Based Orientation Estimation: 3D Orientation Estimation Performance in Disturbed Conditions)

서지정보

· 발행기관 : 제어·로봇·시스템학회
· 수록지 정보 : 제어.로봇.시스템학회 논문지 / 28권 / 12호 / 1216 ~ 1223페이지
· 저자명 : 최지석, 이정근

초록

The estimation of 3D orientation based on nine-axis inertial measurement unit (IMU) is an essential technology in various applications, from unmanned aerial vehicle to human motion tracking. Various sensor fusion filter algorithms such as Kalman filter (KF) or complementary filter (CF) have been proposed for accurate 3D orientation estimation. However, the degradation of estimation performance due to disturbance components such as magnetic distortion and external acceleration is still a critical issue. An alternative approach for the orientation estimation task is to train a neural network end-to-end with a variety of massive experimental datasets consisting of the raw IMU signals and the ground truth orientations. This paper proposes a recurrent neural network (RNN) for robust IMU-based 3D orientation estimation. Overall, this paper is an extension of the previous work by Weber et al., where the RNN model was used to estimate the quaternion, but only the attitude estimation performance was investigated without considering the heading estimation. The proposed RNN receives a nine-dimensional sensor signal as an input and outputs a unit quaternion representing 3D orientation, and it can robustly estimate the orientation across various motion characteristics and magnetic environments without needing an additional filter algorithm. Verification results showed that the proposed method outperformed the conventional CF and KF algorithms. In particular, in magnetically disturbed conditions, the averaged root mean square error of the proposed RNN approach was reduced by 41.4% and 60.3%, respectively, when compared to the CF and the KF algorithm.

영어초록

The estimation of 3D orientation based on nine-axis inertial measurement unit (IMU) is an essential technology in various applications, from unmanned aerial vehicle to human motion tracking. Various sensor fusion filter algorithms such as Kalman filter (KF) or complementary filter (CF) have been proposed for accurate 3D orientation estimation. However, the degradation of estimation performance due to disturbance components such as magnetic distortion and external acceleration is still a critical issue. An alternative approach for the orientation estimation task is to train a neural network end-to-end with a variety of massive experimental datasets consisting of the raw IMU signals and the ground truth orientations. This paper proposes a recurrent neural network (RNN) for robust IMU-based 3D orientation estimation. Overall, this paper is an extension of the previous work by Weber et al., where the RNN model was used to estimate the quaternion, but only the attitude estimation performance was investigated without considering the heading estimation. The proposed RNN receives a nine-dimensional sensor signal as an input and outputs a unit quaternion representing 3D orientation, and it can robustly estimate the orientation across various motion characteristics and magnetic environments without needing an additional filter algorithm. Verification results showed that the proposed method outperformed the conventional CF and KF algorithms. In particular, in magnetically disturbed conditions, the averaged root mean square error of the proposed RNN approach was reduced by 41.4% and 60.3%, respectively, when compared to the CF and the KF algorithm.

참고자료

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