Metric Scale for Insta360 Structure-from-Motion from the Camera's Embedded IMU

Structure-from-motion (SfM) from the dual-fisheye video of a consumer 360-degree camera recovers geometry only up to a similarity transform; in the pipeline considered here, metric scale is obtained from smartphone ARKit or ARCore poses captured alongside the camera. This paper evaluates a self-contained alternative: the IMU embedded in the Insta360 X5 and X6, recorded at 1 kHz. The sensor is characterised (noise density, camera-IMU synchronisation, accelerometer scale factor, bias stability), the camera-IMU rotation and frame timing are estimated from the data, and the scale is recovered by a linear visual-inertial alignment over the SfM poses of a whole survey. The standard formulation, with gravity and accelerometer bias shared across the survey, overestimates the scale by 3.4% on average; estimating each contiguous segment independently and averaging removes the bias. On nine surveys with a co-mounted LiDAR scanner the inertial scale deviates from the LiDAR by 0.74% in mean absolute error, against 1.94% for the smartphone scale; on printed fiducial markers both agree within the 1% uncertainty of the target. On the reference trajectories of thirty sequences of a public benchmark, brought to scales of 1/40, 1 and 40, the estimator recovers the metric scale with a mean error of -0.07% and a standard deviation of 1.28%. On fifty production surveys where the smartphone is the only reference, the two scales agree to a standard deviation of 2.6%, the inertial scale being 0.9% lower on average; the difference does not depend on window construction, grows with the extent of the smartphone anchor, and cannot be attributed to either reference. An ablation over window construction and a comparison with monocular metric-depth networks complete the study.

Authors

Publication Details

Journal
arXiv (Cornell University)
Published
2026-10-26
DOI
https://doi.org/10.48550/arxiv.2610.35554
Primary Topic
Robotics and Sensor-Based Localization
Type
preprint
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preprint

Metric Scale for Insta360 Structure-from-Motion from the Camera's Embedded IMU

Luca Simonetti, Davide Cuttini, Diego Di Tommaso, Alessio Antoniacomi
arXiv (Cornell University)
Robotics and Sensor-Based Localization
preprint

Metric Scale for Insta360 Structure-from-Motion from the Camera's Embedded IMU

Luca Simonetti, Davide Cuttini, Diego Di Tommaso, Alessio Antoniacomi
preprint en

Abstract

Structure-from-motion (SfM) from the dual-fisheye video of a consumer 360-degree camera recovers geometry only up to a similarity transform; in the pipeline considered here, metric scale is obtained from smartphone ARKit or ARCore poses captured alongside the camera. This paper evaluates a self-contained alternative: the IMU embedded in the Insta360 X5 and X6, recorded at 1 kHz. The sensor is characterised (noise density, camera-IMU synchronisation, accelerometer scale factor, bias stability), the camera-IMU rotation and frame timing are estimated from the data, and the scale is recovered by a linear visual-inertial alignment over the SfM poses of a whole survey. The standard formulation, with gravity and accelerometer bias shared across the survey, overestimates the scale by 3.4% on average; estimating each contiguous segment independently and averaging removes the bias. On nine surveys with a co-mounted LiDAR scanner the inertial scale deviates from the LiDAR by 0.74% in mean absolute error, against 1.94% for the smartphone scale; on printed fiducial markers both agree within the 1% uncertainty of the target. On the reference trajectories of thirty sequences of a public benchmark, brought to scales of 1/40, 1 and 40, the estimator recovers the metric scale with a mean error of -0.07% and a standard deviation of 1.28%. On fifty production surveys where the smartphone is the only reference, the two scales agree to a standard deviation of 2.6%, the inertial scale being 0.9% lower on average; the difference does not depend on window construction, grows with the extent of the smartphone anchor, and cannot be attributed to either reference. An ablation over window construction and a comparison with monocular metric-depth networks complete the study.

arXiv (Cornell University)
Robotics and Sensor-Based Localization
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Metric Scale for Insta360 Structure-from-Motion from the Camera's Embedded IMU — Luca Simonetti, Davide Cuttini, et al. · arXiv (Cornell University) (2026) | TGRS Research Map | TGRS