外观
论文arXiv 2609.075161 个月前
P^2Calib：利用模式先验进行 LiDAR-Camera 外参标定
原文链接：P$^2$Calib: Utilizing Pattern Priors for LiDAR-Camera Extrinsic Calibration（arXiv 2609.07516 · 发布日期：2026-09-07）
P$^2$Calib: Utilizing Pattern Priors for LiDAR-Camera Extrinsic Calibration
P^2Calib利用标定板CAD模式先验改进四孔流程，并在仿真与真实数据集上验证外参标定精度提升。
核心要点
- 四孔标定流程受LiDAR端孔中心提取限制，稀疏角覆盖和混合像素会造成中心偏移。
- 方法将已知孔半径作为拟合约束，并加入四孔刚性矩形布局的全局一致性约束。
- 这些先验被集成到交互式工具中，覆盖从目标检测到最终外参的完整流程。
- 在仿真和真实数据集上，联合配准残差较基线降低90%与82%，留出重投影误差降低96%与77%。
验证范围与局限：验证范围为仿真和真实数据集上的目标板LiDAR-Camera外参标定。
英文摘要 (Abstract)
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.
作者：Xiangcheng Hu
来源
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