VR 2017

2017 IEEE Virtual Reality (VR), March 18-22, 2017, Los Angeles, CA, USA

Desktop Layout

Poster Session A
Ballroom A/B
Robust Optical See-Through Head-Mounted Display Calibration: Taking Anisotropic Nature of User Interaction Errors into Account
Ehsan Azimi, Long Qian, Peter Kazanzides, and Nassir Navab
(Johns Hopkins University, USA; TU Munich, Germany)
Abstract: Uncertainty in measurement of point correspondences negatively affects the accuracy and precision in the calibration of head-mounted displays (HMD). Such errors depend on the sensors and pose estimation for video see-through HMD. For optical see-through systems, it additionally depends on the user's head motion and hand-eye coordination. Therefore, the distribution of alignment errors for optical see-through calibration are not isotropic, and one can estimate its process specific or user specific distribution based on interaction requirements of a given calibration process and the user's measurable head motion and hand-eye coordination characteristics. Current calibration methods, however, mostly utilize the DLT method which minimizes Euclidean distances for HMD projection matrix estimation, disregarding the anisotropicity in the alignment errors. We will show how to utilize the error covariance in order to take the anisotropic nature of error distribution into account. The main hypothesis of this study is that using Mahalonobis distance within the nonlinear optimization can improve the accuracy of the HMD calibration. To cover a wide range of possible realistic scenarios, several simulations were performed with variation in the extent of the anisotropicity in the input data along with other parameters. The simulation results indicate that our new method outperforms the standard DLT method both in accuracy and precision, and is more robust against user alignment errors. To the best of our knowledge, this is the first time that anisotropic noise has been accommodated in the optical see-through HMD calibration.


Time stamp: 2020-06-05T04:01:41+02:00