利用目标图案先验提升激光雷达与相机外参标定精度
P$^2$Calib: Utilizing Pattern Priors for LiDAR-Camera Extrinsic Calibration
- 引入孔径半径和矩形布局的几何约束，增强中心点估计
- 实测数据上重投影误差降低77%，联合配准残差减少82%
- 适合需要高精度多传感器融合的机器人系统研发
基于目标的激光雷达-相机外参标定是机器人多传感器融合的前提。然而，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，该过程受稀疏角度覆盖和混合像素干扰影响。本文提出P²Calib，利用目标板的CAD模型所定义的几何先验，改进标定精度。首先，将已知的孔半径作为拟合约束，防止在稀疏视角下中心估计退化；在此基础上，进一步施加四个孔严格矩形排布的全局一致性约束，校正各孔间的残余误差。两个先验被整合进交互式标定工具，形成完整标定流程。在仿真与真实数据集上的实验表明，相比基线方法，P²Calib使联合配准残差降低90%和82%，外推重投影误差降低96%和77%。代码与数据将公开以促进后续研究。
原文摘要 · Abstract (English)
Target-based LiDAR-camera extrinsic calibration is a prerequisite for multi-sensor fusion in robotics. However, in the widely adopted four-hole pipeline, calibration accuracy is bottlenecked by LiDAR-side hole-center extraction, which suffers from sparse angular coverage and mixed-pixel corruption. This paper presents P$^2$Calib, which exploits 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 interactive calibration tool that provides a complete extrinsic calibration pipeline. Experiments on simulated and real datasets show that P$^2$Calib lowers the joint registration residual by 90\% and 82\% and the held-out reprojection error by 96\% and 77\% over the baseline. Code, https://github.com/JokerJohn/P2Calib.git, and data will be released to facilitate future research.
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