Orientation Prediction for VR and AR Devices Using Inertial Sensors Based on Kalman-Like Error Compensation

Le Thi Hue Dao, Truong Thanh Nhat Mai, Wook Hong, Sanghyun Park, Hokwon Kim, Joon Goo Lee, Min Seok Kim, Chul Lee

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)114306-114317
Number of pages12
JournalIEEE Access
Volume10
DOIs
StatePublished - 2022

Keywords

  • attitude and heading reference system (AHRS)
  • augmented reality (AR)
  • inertial measurement units (IMUs)
  • minimum mean square error (MMSE)
  • motion-to-photon (MTP) latency
  • Orientation prediction
  • virtual reality (VR)

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