自动可复现的相机内参标定

中文摘要 AI 辅助

准确的相机内参标定是机器人感知的基础，其精度取决于所采集图像的质量。然而，现有的基于标定板的标定方法通常需要操作者手动筛选高质量图像，并指定合适的径向畸变阶数。本文提出了一种全自动的内参标定流程，能够从采集的数据中同时确定这两者。我们采用一种迭代剔除方案，在候选图像集上估计参数，并移除平均残差超过中位数若干倍的视图。关键在于，该过程在每个候选畸变阶数下独立运行，从而使保留的图像集与该阶数的残差尺度保持一致。此外，畸变阶数在留出图像上进行选择，此时内参和畸变固定，仅重新估计标定板位姿，从而确保新增的系数得到独立观测的支持。最后，我们将这两个步骤集成到一个交互式标定工具中，支持全流程的数据检查和参数估计。在我们自己的相机数据和五个公开的真实世界数据集上的实验表明，图像筛选将留出重投影误差降低了25%，阶数选择进一步降低了5%，在四种比较配置中无需手动图像选择即可实现最低的留出平均误差。我们将发布代码和数据以促进未来的研究。

英文摘要

Accurate camera intrinsic calibration is fundamental to robot perception, and the accuracy depends on the quality of the collected images. However, existing target-based calibration methods often require the practitioner to manually filter out high-quality images and to specify an appropriate radial distortion order. This paper presents a fully automatic intrinsic calibration pipeline that determines both from the collected data. We adopt an iterative rejection scheme that estimates parameters on a candidate image set and removes views whose mean residual exceeds a multiple of the median. Crucially, this process runs independently under each candidate distortion order, so that the retained image set is consistent with the residual scale of that order. Further, the distortion order is selected on held-out images, with the intrinsics and distortion fixed and only the board pose re-estimated, ensuring that an added coefficient is supported by independent observations. Finally, we integrate both steps into an interactive calibration tool that supports full-pipeline data inspection and parameter estimation. Experiments on our own camera data and five public real-world datasets show that image filtering reduces the held-out reprojection error by 25\%, the order selection further by 5\%, achieving the lowest held-out mean among four compared configurations without manual image selection. We will release the code and data to facilitate future research.

补充信息

备注

6 pages, 7 figures