XR Reality Check: What Commercial Devices Deliver For Spatial Tracking
Inaccurate spatial monitoring in extended actuality (XR) devices leads to digital object jitter, misalignment, and user discomfort, essentially limiting immersive experiences and natural interactions. In this work, we introduce a novel testbed that allows simultaneous, synchronized analysis of multiple XR gadgets below identical environmental and kinematic situations. Leveraging this platform, we current the first comprehensive empirical benchmarking of five state-of-the-artwork XR devices across sixteen diverse eventualities. Our results reveal substantial intra-device performance variation, with particular person devices exhibiting as much as 101% will increase in error when working in featureless environments. We additionally show that monitoring accuracy strongly correlates with visual circumstances and motion dynamics. Finally, we discover the feasibility of substituting a movement capture system with the Apple Vision Pro as a practical floor truth reference. 0.387), highlighting both its potential and its constraints for rigorous XR evaluation. This work establishes the primary standardized framework for comparative XR monitoring evaluation, providing the research group with reproducible methodologies, comprehensive benchmark datasets, and open-source instruments that allow systematic analysis of tracking efficiency throughout devices and circumstances, thereby accelerating the development of more robust spatial sensing applied sciences for XR techniques.
The fast advancement of Extended Reality (XR) applied sciences has generated significant curiosity across analysis, growth, and consumer domains. However, inherent limitations persist in visual-inertial odometry (VIO) and visible-inertial SLAM (VI-SLAM) implementations, significantly underneath difficult operational situations together with excessive rotational velocities, low-mild environments, and textureless spaces. A rigorous quantitative evaluation of XR tracking methods is vital for developers optimizing immersive functions and users choosing devices. However, three fundamental challenges impede systematic performance analysis across industrial XR platforms. Firstly, main XR manufacturers do not reveal vital tracking performance metrics, sensor (monitoring camera and IMU) interfaces, or algorithm architectures. This lack of transparency prevents unbiased validation of tracking reliability and limits decision-making by builders and end users alike. Thirdly, existing evaluations concentrate on trajectory-stage performance but omit correlation analyses at timestamp degree that link pose errors to digital camera and IMU sensor information. This omission limits the ability to research how environmental factors and user kinematics influence estimation accuracy.
Finally, most prior work does not share testbed designs or iTagPro Official experimental datasets, limiting reproducibility, validation, and subsequent research, comparable to efforts to mannequin, predict, or adapt to pose errors primarily based on trajectory and sensor information. In this work, we suggest a novel XR spatial monitoring testbed that addresses all of the aforementioned challenges. The testbed enables the next functionalities: (1) synchronized multi-machine monitoring efficiency evaluation under various motion patterns and configurable environmental conditions; (2) quantitative evaluation among environmental characteristics, consumer movement dynamics, multi-modal sensor knowledge, and pose errors; and (3) open-source calibration procedures, knowledge assortment frameworks, and analytical pipelines. Furthermore, our evaluation reveal that the Apple Vision Pro’s monitoring accuracy (with an average relative pose error (RPE) of 0.Fifty two cm, which is the most effective amongst all) permits its use as a ground reality reference for evaluating other devices’ RPE without using a motion seize system. Evaluation to advertise reproducibility and standardized analysis in the XR research neighborhood. Designed a novel testbed enabling simultaneous evaluation of a number of XR devices beneath the same environmental and kinematic situations.
This testbed achieves accurate analysis by way of time synchronization precision and iTagPro Official extrinsic calibration. Conducted the first comparative analysis of 5 SOTA commercial XR gadgets (4 headsets and one pair of glasses), quantifying spatial monitoring efficiency throughout sixteen diverse situations. Our evaluation reveals that average tracking errors fluctuate by up to 2.8× between units under an identical challenging conditions, with errors ranging from sub-centimeter to over 10 cm relying on units, motion types, and atmosphere circumstances. Performed correlation evaluation on collected sensor knowledge to quantify the affect of environmental visible options, SLAM internal status, and IMU measurements on pose error, demonstrating that different XR gadgets exhibit distinct sensitivities to those factors. Presented a case examine evaluating the feasibility of using Apple Vision Pro as a substitute for conventional movement seize methods in monitoring evaluation. 0.387), this suggests that Apple Vision Pro provides a dependable reference for native tracking accuracy, making it a practical instrument for a lot of XR evaluation situations despite its limitations in assessing world pose precision.
