研究工业环境对XR定位系统传感器的失效影响，发现惯性传感器故障危害更大。
Understanding Sensor Vulnerabilities in Industrial XR Tracking
- 通过注入故障测试视觉与惯性传感器在不同工况下的表现
- 视觉退化导致厘米级误差，惯性退化引发数百至数千米偏差
- 提醒工业级XR系统需更关注惯性模块的可靠性设计
部署于工业和操作环境中的扩展现实（XR）系统依赖视觉-惯性里程计（VIO）实现六自由度连续位姿追踪，但这些环境常存在偏离理想假设的传感条件。然而，多数VIO评估仍聚焦于正常传感器行为，对持续退化条件下影响的理解不足。本文开展受控的实证研究，系统考察了视觉与惯性模态在多种运行工况下的故障表现。通过故障注入与量化评估，发现显著不对称性：视觉退化通常导致厘米级位姿误差，而惯性退化可引发数百至数千米的轨迹偏移。该结果凸显在真实工业场景中，需更加重视惯性可靠性在XR系统评估与设计中的作用。
原文摘要 · Abstract (English)
Extended Reality (XR) systems deployed in industrial and operational settings rely on Visual--Inertial Odometry (VIO) for continuous six-degree-of-freedom pose tracking, yet these environments often involve sensing conditions that deviate from ideal assumptions. Despite this, most VIO evaluations emphasize nominal sensor behavior, leaving the effects of sustained sensor degradation under operational conditions insufficiently understood. This paper presents a controlled empirical study of VIO behavior under degraded sensing, examining faults affecting visual and inertial modalities across a range of operating regimes. Through systematic fault injection and quantitative evaluation, we observe a pronounced asymmetry in fault impact where degradations affecting visual sensing typically lead to bounded pose errors on the order of centimeters, whereas degradations affecting inertial sensing can induce substantially larger trajectory deviations, in some cases reaching hundreds to thousands of meters. These observations motivate greater emphasis on inertial reliability in the evaluation and design of XR systems for real-life industrial settings.
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