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Comparing Four Approaches to Generalized Redirected Walking: Simulation and Live User Data

机译:比较广义重定向步行的四种方法:模拟和实时用户数据

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Redirected walking algorithms imperceptibly rotate a virtual scene and scale movements to guide users of immersive virtual environment systems away from tracking area boundaries. These distortions ideally permit users to explore large and potentially unbounded virtual worlds while walking naturally through a physically limited space. Estimates of the physical space required to perform effective redirected walking have been based largely on the ability of humans to perceive the distortions introduced by redirected walking and have not examined the impact the overall steering strategy used. This work compares four generalized redirected walking algorithms, including Steer-to-Center, Steer-to-Orbit, Steer-to-Multiple-Targets and Steer-to-Multiple+Center. Two experiments are presented based on simulated navigation as well as live-user navigation carried out in a large immersive virtual environment facility. Simulations were conducted with both synthetic paths and previously-logged user data. Primary comparison metrics include mean and maximum distances from the tracking area center for each algorithm, number of wall contacts, and mean rates of redirection. Results indicated that Steer-to-Center out-performed all other algorithms relative to these metrics. Steer-to-Orbit also performed well in some circumstances.
机译:重定向的步行算法无法察觉地旋转虚拟场景并缩放运动,以引导沉浸式虚拟环境系统的用户远离跟踪区域边界。理想情况下,这些变形使用户可以在自然受限的空间中自然漫步,探索广阔且可能无限的虚拟世界。进行有效的重定向步行所需的物理空间的估计主要基于人类感知重定向步行所引起的变形的能力,并且没有检查所使用的总体转向策略的影响。这项工作比较了四种广义的重定向步行算法,包括“转向中心”,“转向轨道”,“转向多个目标”和“转向多个+中心”。基于模拟导航以及在大型沉浸式虚拟环境中进行的实时用户导航,提出了两个实验。使用合成路径和先前记录的用户数据进行了仿真。主要比较指标包括每种算法距跟踪区域中心的平均距离和最大距离,墙壁接触的数量以及重定向的平均速率。结果表明,相对于这些指标,“转向中心”的性能优于所有其他算法。转向飞行在某些情况下也表现良好。

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