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

Richard A. Paris, Miti Joshi, Qiliang He, G. Narasimham, T. McNamara, Bobby Bodenheimer
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引用次数: 15

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.
在沉浸式虚拟环境中使用原地行走和重置方法获取调查知识
目前,智能手机驱动的虚拟现实耳机不支持大型虚拟环境中的移动,特别是在有限的物理空间范围内。虽然运动控制器是解决这个问题的一种方法,但它们也存在一些已知的问题:它们占据了受试者的双手,并且会导致糟糕的导航性能。在本文中,我们研究了三种用于导航大型虚拟环境的免提方法。第一种方法是重置,这是一种重新定向技术,允许基于平移和旋转身体的线索。另外两种方法是原地行走技术,只使用基于旋转的线索。在第一个原地行走技术中,我们利用嵌入在三星Gear VR中的智能手机的惯性测量单元来检测受试者何时在行走。第二种技术是使用Kinect的骨骼跟踪来进行步长检测。本文通过测量空间知识的调查分量来评估三种导航条件。我们的度量标准检查对象从其环境中收集和保留信息的情况,以及他们将信息集成到单个模型中的情况。我们发现重置导致最强的调查知识获取,我们认为这是由于该方法提供的前庭线索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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