无需标定板，同时校准相机内参和相机-激光雷达外参。
Joint Target-Less Intrinsic and Extrinsic Camera-LiDAR Calibration using Deep Point Correspondences
- 基于深度点对应关系，自动初始化内参并匹配原始图像与点云。
- 在KITTI数据集上实现外参更精准校准，并准确恢复内参参数。
- 适合无标定板场景下的机器人多模态感知系统开发。
精确的相机-激光雷达标定是机器人多模态感知的基础。现有无标定板方法基于深度点对应关系，在已知内参且图像已校正的条件下可实现优异的外参标定，但无法处理未知内参及畸变的情况。本文提出首个完全无标定板的联合标定流程，同时估计相机内参（针孔模型带径向-切向畸变）与相机-激光雷达外参。方法通过结构自运动（SfM）自动初始化内参，将相机-激光雷达匹配推广至含未知内参和畸变的原始图像，并将点对应估计与内外参联合非线性优化紧密耦合。在未见过的相机-激光雷达配对的KITTI数据集上验证，该方法不仅提升了外参精度，还成功恢复了高精度的内参。
原文摘要 · Abstract (English)
Accurate camera-LiDAR calibration is a prerequisite for robust multi-modal perception in robotics. Recent target-less approaches based on deep point correspondences achieve remarkable performance for extrinsic calibration but assume rectified images with known intrinsics. In this work, we overcome this limitation and present the first fully target-less pipeline that jointly estimates camera intrinsics (pinhole model with radial-tangential distortion) and camera-LiDAR extrinsics with deep pixel-point correspondences. Our approach extends deep correspondence-based calibration by (i) automatic intrinsic initialization via structure-from-motion, (ii) generalizing camera-LiDAR matching to raw images with unknown intrinsics including distortion, and (iii) tightly coupling correspondence estimation with joint nonlinear optimization over both intrinsics and extrinsics. We evaluate our method on the KITTI dataset with unseen camera-LiDAR pairs and demonstrate that joint calibration achieves improved extrinsic accuracy while additionally recovering accurate intrinsics.
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