Monte-Carlo Redirected Walking: Gain Selection Through Simulated Walks.

Abstract

We present Monte-Carlo Redirected Walking (MCRDW), a gain selection algorithm for redirected walking. MCRDW applies the Monte-Carlo method to redirected walking by simulating a large number of simple virtual walks, then inversely applying redirection to the virtual paths. Different gain levels and directions are applied, producing differing physical paths. Each physical path is scored and the results used to select the best gain level and direction. We provide a simple example implementation and a simulation-based study for validation. In our study, when compared with the next best technique, MCRDW reduced incidence of boundary collisions by over 50% while reducing total rotation and position gain.