GravCal: Single-Image Calibration of IMU Gravity Priors with Per-Sample Confidence

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: GravCal reduces mean angular error from 22.02{\deg} (IMU prior) to 14.24{\deg}, with larger improvements when the prior is severely corrupted. We also introduce a novel dataset of over 148K frames with paired VIO-derived ground-truth gravity and Mahony-filter IMU priors across diverse scenes and arbitrary camera orientations. The learned gate also correlates with prior quality, making it a useful confidence signal for downstream systems.