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://ganuna.umd.edu/arc/ .

中文翻译：

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ARC：适用于复杂环境中重定向步行的基于路线的重定向控制器

我们提出了一种基于对齐的新颖的重定向步行控制器，该控制器允许用户探索大型复杂的虚拟环境，同时最大程度地减少物理环境中与障碍物的碰撞次数。我们基于路线的重定向控制器ARC可以指导用户，使其与物理环境中的障碍物的距离尽可能接近虚拟环境中与障碍物的距离。为了量化控制器在复杂环境中的性能，我们引入了一种新的度量标准，即“复杂度比率”（CR），以测量相对环境的复杂度并表征物理和虚拟环境之间导航复杂度的差异。通过大量基于模拟的实验，我们证明ARC在引导用户使用无冲突路径方面的能力大大优于当前的最新控制器。我们还通过定量和定性的性能指标来表明，我们的控制器在复杂的环境中（具有许多障碍）是强大的。我们的方法适用于任意环境，并且除了环境布局之外，无需任何用户输入或参数调整即可运行。我们已经在Oculus Quest头戴式显示器上实现了我们的算法，并评估了其在复杂程度不同的环境中的性能。我们的项目网站位于：我们的方法适用于任意环境，并且除了环境布局之外，无需任何用户输入或参数调整即可运行。我们已经在Oculus Quest头戴式显示器上实现了我们的算法，并在复杂程度不同的环境中评估了其性能。我们的项目网站可在以下网址找到：我们的方法适用于任意环境，并且除了环境布局之外，无需任何用户输入或参数调整即可运行。我们已经在Oculus Quest头戴式显示器上实现了我们的算法，并在复杂程度不同的环境中评估了其性能。我们的项目网站可在以下网址找到：https://ganuna.umd.edu/arc/ 。