Improved Redirection with Distractors: A large-scale-real-walking locomotion interface and its effect on navigation in virtual environments

Abstract

Users in virtual environments often find navigation more difficult than in the real world. Our new locomotion interface, Improved Redirection with Distractors (IRD), enables users to walk in larger-than-tracked space VEs without predefined waypoints. We compared IRD to the current best interface, really walking, by conducting a user study measuring navigational ability. Our results show that IRD users can really walk through VEs that are larger than the tracked space and can point to targets and complete maps of VEs no worse than when really walking.

A system for exploring large virtual environments that combines scaled translational gain and interventions

“…There were no other main effects or interactions. Note that the baseline mean turning error of about 22 • in the 1:1 condition is consistent with errors in the turn-to-point modality in virtual environment experiments, both in our group's prior work, e.g., Williams et al 2006] and in other groups' work doing similar experiments, e.g., [Chance et al 1998;Kelly et al 2009;Kuhl et al 2008;Peck et al 2010].…”

“…In this method, they determine a user's intended direction and scale the gain of that direction only using a wand control. Peck et al [2009;2010] used distractors--objects in the virtual environment for the users to focus on while the virtual environment rotates-in methods that are closely akin to our own. While the use of distractor is interesting, it is not clear what it brings to the environment in terms of a user's ability to spatially update.…”

NaviFields

“…Techniques proposed make use of rotational gains [24], translation gain [39], space substitution [32] or distractors [23]. However, the tracking spaces needed are still large (e.g.…”

Flexible spaces: Dynamic layout generation for infinite walking in virtual environments

“…The most basic approach is to stop the user at the boundary of the tracked space and instruct him to perform head turns in order to unnoticeably rotate the scene [6]. Other methods include introducing scene rotation with or without warning [7], or while the user is distracted by an unexpected event [8], [9]. Circular algorithms keep a user on a circular trajectory by returning him from outside or keeping him inside a circle during rotation in order to bring the user back to the center of the tracked space [8].…”

“…Circular algorithms keep a user on a circular trajectory by returning him from outside or keeping him inside a circle during rotation in order to bring the user back to the center of the tracked space [8]. On the other side, rotation can be applied constantly on an unperceivable level as in [9] or by using system gains proportionally to the change in users' position and orientation [3]. In [3] the human sensitivity was evaluated towards changes of the gains from initial one-to-one mapping.…”