[DC] Maximizing Natural Walking in Virtual Environments

Abstract

Being restricted to small physical spaces limits the practicability of real walking in extensive virtual areas. Thus, teleportation or other locomotion metaphors are needed to fully navigate such virtual spaces. Nevertheless, since natural walking reflects the naturalness and intuitiveness of human locomotion well, it is still considered the best locomotion method. Therefore, researchers have mainly focused on developing concepts to increase the applicability of real walking in virtual spaces exceeding the physical space availability. These redirection methods mainly consist of overlapping spaces, redirected walking, and change blindness. Some of these methods have been extended to environments with multiple users. However, most literature considers only one method at the time applied to single users. Since this work focuses on the maximization of the natural walking experience, the combination of RDW in overlapping spaces with optimized reset strategies may complement and thus improve the final user experience. Further, most of previous approaches presume the boundaries of the physical space to be known beforehand and that this space is convex and free of obstacles. In my Ph.D. work, I will first look into combining the existing redirection methods in a known tracking space to maximize the walking distances that multi-users can navigate without colliding. In a subsequent step, I will extend these methods to an unknown and non-convex physical space. To discover and thus expand the available physical space, I will look into various exploration strategies that allow efficient space discovery. Developing the two previous ideas would only reduce the collision risk, accordingly, resets are still necessary. Hence, I will further investigate new reset strategies based on path prediction that allow potentially resetting users in early stages before they are even close to obstacles or boundaries, strategically reducing future resets.