配准误差对远距离增强目标跟踪的影响

M. Livingston, Zhuming Ai
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引用次数: 40

摘要

我们对注册误差对增强现实中远距离目标跟踪性能的影响进行了用户研究。根据通常用作规范的类型对错误进行分类,我们希望能够深入了解用户容忍噪音、延迟和方向错误的能力。我们使用实际系统的测量结果来推导参数设置。我们预计这三种错误都会影响用户正确执行任务的能力和他们执行任务的精度。我们发现,高延迟对性能和响应时间都有负面影响。虽然噪声始终与其他变量相互作用,并且方向误差增加了用户误差,但ldquohighrdquo和ldquolowrdquo之间的差异比我们预期的要小。用户对这三类错误的主观排名结果令人惊讶地参差不齐。用户认为噪音是最有害的,尽管对性能的统计分析驳斥了这种观点。我们解释结果并为系统设计提供见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The effect of registration error on tracking distant augmented objects
We conducted a user study of the effect of registration error on performance of tracking distant objects in augmented reality. Categorizing error by types that are often used as specifications, we hoped to derive some insight into the ability of users to tolerate noise, latency, and orientation error. We used measurements from actual systems to derive the parameter settings. We expected all three errors to influence userspsila ability to perform the task correctly and the precision with which they performed the task. We found that high latency had a negative impact on both performance and response time. While noise consistently interacted with the other variables, and orientation error increased user error, the differences between ldquohighrdquo and ldquolowrdquo amounts were smaller than we expected. Results of userspsila subjective rankings of these three categories of error were surprisingly mixed. Users believed noise was the most detrimental, though statistical analysis of performance refuted this belief. We interpret the results and draw insights for system design.
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