TY - JOUR
T1 - Orientation Prediction for VR and AR Devices Using Inertial Sensors Based on Kalman-Like Error Compensation
AU - Hue Dao, Le Thi
AU - Mai, Truong Thanh Nhat
AU - Hong, Wook
AU - Park, Sanghyun
AU - Kim, Hokwon
AU - Lee, Joon Goo
AU - Kim, Min Seok
AU - Lee, Chul
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2022
Y1 - 2022
N2 - We propose an orientation prediction algorithm based on Kalman-like error compensation for virtual reality (VR) and augmented reality (AR) devices using measurements of an inertial measurement unit (IMU), which includes a tri-axial gyroscope and a tri-axial accelerometer. First, the initial prediction of the orientation is estimated by assuming linear movement. Then, to improve the prediction accuracy, the accuracies of previous predictions are taken into account by computing the orientation difference between the current orientation and previous prediction. Finally, we define a weight matrix to determine the optimal adjustments for predictions corresponding to a given orientation, which is obtained by minimizing the estimation errors based on the minimum mean square error (MMSE) criterion using Kalman-like error compensation. Experimental results demonstrate that the proposed algorithm exhibits higher orientation prediction accuracy compared with conventional algorithms on several open datasets.
AB - We propose an orientation prediction algorithm based on Kalman-like error compensation for virtual reality (VR) and augmented reality (AR) devices using measurements of an inertial measurement unit (IMU), which includes a tri-axial gyroscope and a tri-axial accelerometer. First, the initial prediction of the orientation is estimated by assuming linear movement. Then, to improve the prediction accuracy, the accuracies of previous predictions are taken into account by computing the orientation difference between the current orientation and previous prediction. Finally, we define a weight matrix to determine the optimal adjustments for predictions corresponding to a given orientation, which is obtained by minimizing the estimation errors based on the minimum mean square error (MMSE) criterion using Kalman-like error compensation. Experimental results demonstrate that the proposed algorithm exhibits higher orientation prediction accuracy compared with conventional algorithms on several open datasets.
KW - attitude and heading reference system (AHRS)
KW - augmented reality (AR)
KW - inertial measurement units (IMUs)
KW - minimum mean square error (MMSE)
KW - motion-to-photon (MTP) latency
KW - Orientation prediction
KW - virtual reality (VR)
UR - http://www.scopus.com/inward/record.url?scp=85141452687&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2022.3217555
DO - 10.1109/ACCESS.2022.3217555
M3 - Article
AN - SCOPUS:85141452687
SN - 2169-3536
VL - 10
SP - 114306
EP - 114317
JO - IEEE Access
JF - IEEE Access
ER -