Consumer level virtual experiences almost always occur when physical space is limited, either by the constraints of an indoor space or of a tracked area. This observation coupled with the need for movement through large virtual spaces has resulted in a proliferation of research into locomotion interfaces that decouples movement through the virtual environment from movement in the real world. While many locomotion interfaces support movement of some kind in the real world, some do not. This paper examines the effect of the amount of physical space used in the real world on one popular locomotion interface, resetting, when compared to a locomotion interface that requires minimal physical space, walking in place. The metric used to compare the two locomotion interfaces was navigation performance, specifically, the acquisition of survey knowledge. We find that, while there are trade-offs between the two methods, walking in place is preferable in small spaces.

Navigation, or the means by which people find their way in an environment, depends on the ability to combine information from multiple sources so that properties of an environment, such as the location of a goal, can be estimated. An important source of information for navigation are spatial cues generated by self-motion. Navigation based solely on body-based cues generated by self-motion is called path integration. In virtual reality and video games, many locomotion systems, that is, methods that move users through a virtual environment, can often distort or deprive users of important self-motion cues. There has been much study of this issue, and in this paper, we extend that study in novel directions by assessing the effect of four game-like locomotion interfaces on navigation performance using path integration. The salient features of our locomotion interfaces are that two are primarily continuous, i.e., more like a joystick, and two are primarily discrete, i.e., more like teleportation. Our main findings are that the perspective of path integration, people are able to use all methods, although continuous methods outperform discrete methods.

Walking in place is a standard method for moving through large virtual environments when physical space or positional tracking is limited. This technique has become increasingly prominent with the advent of mobile virtual reality in which external tracking may not be present. In this paper, we revisit walking in place algorithms to address some of their technical challenges. Namely, our solutions attend to improving starting, stopping, and speed control for individual users. From a hand-tuned threshold based algorithm, we provide a new, fast method for individualizing the walking in place algorithm based on biomechanic measures of step rate. In addition, we introduce a new walking in place model based on a convolutional neural network trained to differentiate walking and standing. Over two experiments we assess these methods against a traditional threshold based algorithm on two mobile virtual reality platforms. The assessments are based on controllability, scale, and presence. Our results suggest that an adequately trained convolutional neural network can be an effective way of implementing walking in place.

Most virtual environments that people locomote through with head-mounted displays are flat to match the physical environment that people are actively walking on. In this paper we simulated stair climbing, and evaluated how well people could assess the distance they had climbed after several minutes of the activity under various conditions. We varied factors such as the presence of virtual feet (shoes), whether the stairwell was open or enclosed, the presence or absence of passive haptic markers, and whether a subject was ascending or descending. In general, the distance climbed or descended was overestimated, consistent with prior work on the perception of height. We find that subjects have significantly better ability to estimate their error with the presence of virtual shoes than without, and when the environment was open. Having shoes also resulted in significantly higher ratings of presence. We also find a significant tendency for females to show higher ratings of simulator sickness.

The utility of mediated environments increases when environmental scale (size and distance) is perceived accurately. We present the use of perceived affordances---judgments of action capabilities---as an objective way to assess space perception in an augmented reality (AR) environment. The current study extends the previous use of this methodology in virtual reality (VR) to AR. We tested two locomotion-based affordance tasks. In the first experiment, observers judged whether they could pass through a virtual aperture presented at different widths and distances, and also judged the distance to the aperture. In the second experiment, observers judged whether they could step over a virtual gap on the ground. In both experiments, the virtual objects were displayed with the HoloLens in a real laboratory environment. We demonstrate that affordances for passing through and perceived distance to the aperture are similar in AR to those measured in the real world, but that judgments of gap-crossing in AR were underestimated. These differences across two affordances may result from the different spatial characteristics of the virtual objects (on the ground versus extending off the ground).

Locomotion in large virtual environments is currently unsupported in smartphone-powered virtual reality headsets, particularly within the confines of limited physical space. While motion controllers are a workaround for this issue, they exhibit known problems: they occupy the subject's hands, and they cause poor navigation performance. In this paper, we investigate three hands-free methods for navigating large virtual environments. The first method is resetting, a reorientation technique that allows for both translation and rotation body-based cues. The other two methods are walking in place techniques that use only rotation-based cues. In the first walking in place technique, we make use of the inertial measurement unit of the smartphone embedded in a Samsung Gear VR to detect when subjects are stepping. The second technique uses the Kinect's skeletal tracking for step detection. In this paper, we measure the survey component of spatial knowledge to assess three navigation conditions. Our metrics examine how well subjects gather and retain information from their environment, as well as how well they integrate it into a single model. We find that resetting leads to the strongest acquisition of survey knowledge, which we believe is due to the vestibular cues provided by this method.

Interesting virtual environments that permit free exploration are rarely small. A number of techniques have been developed to allow people to walk in larger virtual spaces than permitted by physical extent of the virtual reality hardware, and in this paper we compare three such methods in terms of how they affect presence and spatial awareness. In our first psychophysical study, we compared two methods of reorientation and one method of redirected walking on subjects' presence and spatial memory while navigating a pre-specified path. We further compared the two reorientation methods in a second psychophysical study involving free exploration and navigation in a large virtual environment. Our results provide criteria by which the choice of a locomotion method for navigating large virtual environments may be selected.

When two eyes view different objects or scenes at the same time, stable binocular single vision gives way to alternations in perception. Called binocular rivalry, this beguiling phenomenon discloses the existence of inhibitory interactions between neural representations associated with the conflicting visual inputs. One strategy for learning details about this neural competition is to ascertain to what extent it is susceptible to top-down influences such as expectations engendered by one's own activity [Maruya et al. 2007]. Thanks to advances in virtual reality (VR) technology, this strategy can be implemented in the laboratory, which we have done. Specifically we are measuring the extent to which self-generated motion (walking) biases perception during binocular rivalry between two competing visual optic flow fields (one specifying forward locomotion and the other specifying backward locomotion). Our progress to date is reported in this poster.