Understanding Sensor Vulnerabilities in Industrial XR Tracking

TL;DR

This paper empirically investigates how sensor degradation affects Visual--Inertial Odometry in industrial XR systems, revealing that inertial sensor faults cause significantly larger pose errors than visual faults, highlighting the need for improved inertial reliability.

Contribution

It provides a systematic empirical analysis of VIO under sensor faults, emphasizing the asymmetric impact of visual and inertial sensor degradation in industrial XR environments.

Findings

Visual sensor faults cause bounded pose errors of centimeters.

Inertial sensor faults can lead to trajectory deviations of hundreds to thousands of meters.

Inertial reliability is crucial for robust XR system performance in industrial settings.

Abstract

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.