GravCal: Single-Image Calibration of IMU Gravity Priors with Per-Sample Confidence
Haichao Zhu, Qian Zhang

TL;DR
GravCal is a novel feedforward model that calibrates noisy gravity priors from a single RGB image, significantly improving accuracy and providing confidence estimates, which benefits visual-inertial perception and robotics.
Contribution
The paper introduces GravCal, a new method that combines residual correction and image-based estimation to calibrate gravity priors from a single image, with adaptive fusion and confidence scoring.
Findings
Reduces mean angular error from 22.02° to 14.24°
Performs well even with severely corrupted priors
Provides a confidence score correlated with prior quality
Abstract
Gravity estimation is fundamental to visual-inertial perception, augmented reality, and robotics, yet gravity priors from IMUs are often unreliable under linear acceleration, vibration, and transient motion. Existing methods often estimate gravity directly from images or assume reasonably accurate inertial input, leaving the practical problem of correcting a noisy gravity prior from a single image largely unaddressed. We present GravCal, a feedforward model for single-image gravity prior calibration. Given one RGB image and a noisy gravity prior, GravCal predicts a corrected gravity direction and a per-sample confidence score. The model combines two complementary predictions, including a residual correction of the input prior and a prior-independent image estimate, and uses a learned gate to fuse them adaptively. Extensive experiments show strong gains over raw inertial priors:…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Vision and Imaging · Optical measurement and interference techniques
