Acquisition of survey knowledge using walking in place and resetting methods in immersive virtual environments

Abstract

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.