We present a novel redirected walking controller based on alignment that allows the user to explore large and complex virtual environments, while minimizing the number of collisions with obstacles in the physical environment. Our alignment-based redirection controller, ARC, steers the user such that their proximity to obstacles in the physical environment matches the proximity to obstacles in the virtual environment as closely as possible. To quantify a controller's performance in complex environments, we introduce a new metric, Complexity Ratio (CR), to measure the relative environment complexity and characterize the difference in navigational complexity between the physical and virtual environments. Through extensive simulation-based experiments, we show that ARC significantly outperforms current state-of-the-art controllers in its ability to steer the user on a collision-free path. We also show through quantitative and qualitative measures of performance that our controller is robust in complex environments with many obstacles. Our method is applicable to arbitrary environments and operates without any user input or parameter tweaking, aside from the layout of the environments. We have implemented our algorithm on the Oculus Quest head-mounted display and evaluated its performance in environments with varying complexity. Our project website is available at https://gamma.umd.edu/arc/.
翻译:我们展示了一个基于对齐的新的调整方向行走控制器,使用户能够探索大而复杂的虚拟环境,同时最大限度地减少与物理环境中的障碍相撞的次数。我们的校正调整方向控制器ARC指导用户,使其接近物理环境中的障碍,尽可能接近虚拟环境中的障碍。为了量化一个控制器在复杂环境中的性能,我们引入了新的度量度、复杂性比率(CR),以测量相对环境的复杂程度,并辨别物理环境和虚拟环境之间航行复杂性的差异。通过广泛的模拟实验,我们显示ARC在引导用户走上无碰撞道路的能力方面,明显优于目前的最新状态。我们还通过定量和定性的性能衡量方法显示,我们的控制器在复杂的环境中非常活跃,有许多障碍。我们的方法适用于任意环境,除了环境的布局外,没有用户输入或参数节纹。我们已在Oculus Quest-head-droad 显示并评估其在不同复杂环境中的性能。我们的项目网站可访问 http:// httpsurus/delamma.