Enabling Predictive Redirection Reset Based on Virtual-Real Spatial Probability Density Distributions.

Abstract

Redirected walking (RDW) allows users to explore vast virtual spaces by walking in confined real spaces, yet suffers from frequent boundary collisions due to physical constraints. The major solution is to use the reset strategy to steer users away from boundaries. However, most reset methods guide users to fixed spots or follow constant patterns, neglecting spatial features and users' movement trends. In this paper, we propose an innovative predictive reset method based on spatial probability density distribution to jointly involve impacts of spatial feature and walking intention for forecasting the user's possible positional distribution, and thereby determines the optimal reset direction by maximizing walking expectation. Given a space, we calculate the stationary layout energy to indicate traveling difficulties of all positions. Meanwhile, we exploit a novel intention inference model to anticipate the probability distribution of the user's presence across adjacent positions. Furthermore, we incorporate the obstacle energy attenuation to predict the obstacle avoidance behaviors. All aforementioned factors are amalgamated into a potential region energy map, and then we integrate energy maps of virtual and real spaces into a fusion energy map to enable the prediction considering both spaces simultaneously. Thus, the optimal reset direction is derived by maximizing the fusion energy. Simulation and user studies are conducted on a broad dataset containing plentiful virtual and real spaces. The results demonstrate that our method effectively reduces the physical collisions and increase the continuous walking distance compared to prevalent reset methods, while exhibiting superior applicability when combined with various RDW controllers. The source code and dataset are available at https://github.com/huiyuroy/Return2MaxPotentialEnergy.