用单张图像校准陀螺仪的重力方向，提升机器人定位精度。
GravCal: Single-Image Calibration of IMU Gravity Priors with Per-Sample Confidence
- 输入图像和噪声重力数据，输出修正后的重力方向与置信度。
- 平均角度误差从22.02°降至14.24°，尤其在严重干扰下效果显著。
- 自适应融合两种预测方式，置信度可指导下游系统使用。
重力估计对视觉惯性感知、增强现实和机器人技术至关重要，但惯性测量单元（IMU）在直线加速、振动和瞬时运动下提供的重力先验常不可靠。现有方法或直接从图像估计重力，或假设惯性输入足够准确，未能解决仅用单张图像修正噪声重力先验的问题。本文提出GravCal，一种前馈模型，给定一张RGB图像和一个噪声重力先验，可预测修正后的重力方向及每样本置信度。该模型结合残差修正与独立于先验的图像估计，并通过学习门控机制自适应融合。大量实验表明，相较于原始惯性先验，GravCal将平均角度误差从22.02°降至14.24°，尤其在先验严重失真时提升更大。我们还构建了一个包含超过14.8万帧的新数据集，涵盖多种场景与任意相机姿态，提供基于VIO的真值重力与Mahony滤波器生成的IMU先验。学习到的门控机制与先验质量相关，可作为下游系统的有效置信信号。
原文摘要 · Abstract (English)
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° (IMU prior) to 14.24°, 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.
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