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For autonomous vehicles, an accurate calibration for LiDAR and camera is aprerequisite for multi-sensor perception systems. However, existing calibrationtechniques require either a complicated setting with various calibrationtargets, or an initial calibration provided beforehand, which greatly impedestheir applicability in large-scale autonomous vehicle deployment. To tacklethese issues, we propose a novel method to calibrate the extrinsic parameterfor LiDAR and camera in road scenes. Our method introduces line features fromstatic straight-line-shaped objects such as road lanes and poles in both imageand point cloud and formulates the initial calibration of extrinsic parametersas a perspective-3-lines (P3L) problem. Subsequently, a cost function definedunder the semantic constraints of the line features is designed to performrefinement on the solved coarse calibration. The whole procedure is fullyautomatic and user-friendly without the need to adjust environment settings orprovide an initial calibration. We conduct extensive experiments on KITTI andour in-house dataset, quantitative and qualitative results demonstrate therobustness and accuracy of our method.

CRLF: Automatic Calibration and Refinement based on Line Feature for LiDAR and Camera in Road Scenes