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：我们提出了一种新颖的基于对齐的重定向行走控制器，使用户能够探索大型复杂虚拟环境，同时尽可能减少与物理环境中障碍物的碰撞次数。我们的基于对齐的重定向控制器ARC通过引导用户，使其在物理环境中与障碍物的接近程度尽可能匹配虚拟环境中与障碍物的接近程度。为了量化控制器在复杂环境中的性能，我们引入了一个新指标——复杂度比（CR），用以测量相对环境复杂度并刻画物理环境与虚拟环境之间导航复杂度的差异。通过大量基于仿真的实验，我们证明ARC在引导用户沿无碰撞路径行走的能力上显著优于当前最优控制器。我们还通过定量和定性性能度量表明，我们的控制器在具有众多障碍物的复杂环境中具有鲁棒性。该方法适用于任意环境，且除环境布局外无需任何用户输入或参数调整。我们已在Oculus Quest头戴式显示器上实现该算法，并在不同复杂度环境中评估其性能。项目网站见https://gamma.umd.edu/arc/。