  For autonomous vehicles, an accurate calibration for LiDAR and camera is a
prerequisite for multi-sensor perception systems. However, existing calibration
techniques require either a complicated setting with various calibration
targets, or an initial calibration provided beforehand, which greatly impedes
their applicability in large-scale autonomous vehicle deployment. To tackle
these issues, we propose a novel method to calibrate the extrinsic parameter
for LiDAR and camera in road scenes. Our method introduces line features from
static straight-line-shaped objects such as road lanes and poles in both image
and point cloud and formulates the initial calibration of extrinsic parameters
as a perspective-3-lines (P3L) problem. Subsequently, a cost function defined
under the semantic constraints of the line features is designed to perform
refinement on the solved coarse calibration. The whole procedure is fully
automatic and user-friendly without the need to adjust environment settings or
provide an initial calibration. We conduct extensive experiments on KITTI and
our in-house dataset, quantitative and qualitative results demonstrate the
robustness and accuracy of our method.
