AI RESEARCH
GravCal: Single-Image Calibration of IMU Gravity Priors with Per-Sample Confidence
arXiv CS.CV
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ArXi:2603.19654v1 Announce Type: new 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.