Plane-Guided Point-Line LiDAR–Camera Extrinsic Calibration with Finite Line-Segment Constraints

Light detection and ranging (LiDAR)–camera calibration requires reliable target reconstruction and robust pose estimation. We present P3LPnP, a plane-guided point-line framework that registers a known-size checkerboard lattice and jointly optimizes point re-projection and finite-segment length, direction, and endpoint position. The front end combines random sample consensus (RANSAC), density-based spatial clustering of applications with noise (DBSCAN), and principal component analysis (PCA). The back end uses iteratively re-weighted least squares with weights fixed during inner solves and exact-Huber step acceptance. In 30 matched synthetic scenes, P3LPnP achieved a plane root mean square error of 0.673 mm. Relative to point-only optimization, finite-segment constraints reduced mean clean-reference re-projection error from 0.441 to 0.399 pixels and translation error from 5.094 to 3.424 mm. Synthetic tests showed translation robustness to point noise and coherent bias, with competitive performance under sparse joint noise. A 30-run controlled replay from one system yielded mean re-projection errors of 0.356 and 0.338 pixels on 100 sparse reference points and 9900 same-session held-out points. Real-data evidence supported same-system consistency; the advantage over the point-line baseline was not statistically significant, and multi-system generalizability remained unverified.