发表机构
The Hong Kong University of Science and Technology（香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对激光雷达-相机外参标定中孔中心提取精度受限问题，提出利用CAD模型几何先验（孔半径和矩形布局）的P$^2$Calib方法，显著降低配准残差和重投影误差。
AI 中文摘要
基于靶标的激光雷达-相机外参标定是机器人多传感器融合的前提。然而，在广泛采用的四孔标定流程中，标定精度受限于激光雷达侧孔中心提取，该步骤面临稀疏角度覆盖和混合像素干扰的问题。本文提出P$^2$Calib，利用图案先验（即由靶标板CAD模型指定的几何约束）来提高标定精度。首先，我们将已知的孔半径作为拟合约束，以防止在稀疏角度覆盖下中心估计退化。在改进的孔估计基础上，我们进一步将四孔的刚性矩形布局作为全局一致性约束，以校正跨孔的残余误差。这两种先验被集成到一个交互式标定工具中，提供完整的外参标定流程。在模拟和真实数据集上的实验表明，与基线相比，P$^2$Calib将联合配准残差降低了90%和82%，将留出重投影误差降低了96%和77%。代码、此https URL和数据将发布以促进未来研究。
英文摘要
Target-based LiDAR-camera extrinsic calibration is a prerequisite for multi-sensor fusion in robotics. However, in the widely adopted four-hole pipeline, \rt{calibration accuracy is limited by hole-center extraction in the LiDAR side, where sparse angular coverage and mixed-pixel returns displace the estimated centers}. This paper presents P$^2$Calib, which exploits \textit{pattern priors}, geometric constraints specified by the CAD model of the target board, to improve calibration accuracy. First, we incorporate the known hole radius as a fitting constraint to prevent center estimates from degrading under sparse angular coverage. Building on the improved hole estimates, we further enforce the rigid rectangular layout of the four holes as a global consistency constraint to correct residual errors across holes. Both priors are integrated into an \rt{interactive tool that runs the pipeline from target detection to the final extrinsic}. Experiments on simulated and real datasets show that P$^2$Calib reduces the joint registration residual by 90\% and 82\% and the held-out reprojection error by 96\% and 77\% over the baseline. \rt{We will release the code and data\codelink{} to facilitate future research.
Comments12 pages, 10 figures
