Inaccurate spatial monitoring in prolonged reality (XR) gadgets results in virtual object jitter, misalignment, and user discomfort, basically limiting immersive experiences and pure interactions. On this work, we introduce a novel testbed that allows simultaneous, synchronized evaluation of a number of XR gadgets below similar environmental and kinematic situations. Leveraging this platform, we current the primary comprehensive empirical benchmarking of five state-of-the-artwork XR units throughout sixteen numerous eventualities. Our outcomes reveal substantial intra-machine performance variation, with particular person units exhibiting up to 101% increases in error when operating in featureless environments. We also show that tracking accuracy strongly correlates with visual situations and motion dynamics. Finally, we explore the feasibility of substituting a movement seize system with the Apple Vision Pro as a sensible ground truth reference. 0.387), highlighting each its potential and its constraints for rigorous XR evaluation. This work establishes the primary standardized framework for comparative XR tracking evaluation, providing the research neighborhood with reproducible methodologies, comprehensive benchmark datasets, and open-supply instruments that allow systematic analysis of monitoring performance throughout units and conditions, thereby accelerating the development of extra strong spatial sensing technologies for XR techniques.

The fast development of Extended Reality (XR) applied sciences has generated vital curiosity across analysis, development, and shopper domains. However, inherent limitations persist in visual-inertial odometry (VIO) and iTagPro tracker visible-inertial SLAM (VI-SLAM) implementations, particularly underneath difficult operational circumstances together with high rotational velocities, itagpro tracker low-gentle environments, and textureless areas. A rigorous quantitative analysis of XR tracking systems is vital for developers optimizing immersive applications and customers choosing devices. However, three fundamental challenges impede systematic performance evaluation across commercial XR platforms. Firstly, main XR manufacturers don't reveal critical monitoring efficiency metrics, sensor (tracking camera and IMU) interfaces, or iTagPro product algorithm architectures. This lack of transparency prevents impartial validation of tracking reliability and limits resolution-making by developers and finish customers alike. Thirdly, current evaluations deal with trajectory-level efficiency but omit correlation analyses at timestamp degree that hyperlink pose errors to digital camera and IMU sensor data. This omission limits the power to investigate how environmental components and consumer kinematics influence estimation accuracy.

Finally, most prior work does not share testbed designs or experimental datasets, limiting reproducibility, validation, and subsequent analysis, similar to efforts to model, predict, or adapt to pose errors based on trajectory and sensor data. In this work, we propose a novel XR spatial monitoring testbed that addresses all of the aforementioned challenges. The testbed allows the next functionalities: (1) synchronized multi-gadget monitoring efficiency analysis under numerous motion patterns and configurable environmental situations

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Pub: 11 Sep 2025 07:56 UTC

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