Understanding Sensor Vulnerabilities in Industrial XR Tracking
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Abstract (arXiv)
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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References · 10
- 2020ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAMcited by 862
- 2019Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mappingcited by 181
- 2021GVINS: Tightly Coupled GNSS-Visual-Inertial Fusion for Smooth and Consistent State Estimationcited by 17
- 2023RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environmentscited by 9
- 2023VIO-DualProNet: Visual-Inertial Odometry with Learning Based Process Noise Covariancecited by 6
- 2025OKVIS2-X: Open Keyframe-based Visual-Inertial SLAM Configurable with Dense Depth or LiDAR, and GNSScited by 4
- 2022EqVIO: An Equivariant Filter for Visual Inertial Odometrycited by 3
- 2025Detection and Recovery of Adversarial Slow-Pose Drift in Offloaded Visual-Inertial Odometrycited by 3
- 2025XR-VIO: High-precision Visual Inertial Odometry with Fast Initialization for XR Applicationscited by 2
- 2024PO-MSCKF: An Efficient Visual-Inertial Odometry by Reconstructing the Multi-State Constrained Kalman Filter with the Pose-only Theorycited by 1
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