  While camera and LiDAR are widely used in most of the assisted and autonomous
driving systems, only a few works have been proposed to associate the temporal
synchronization and extrinsic calibration for camera and LiDAR which are
dedicated to online sensors data fusion. The temporal and spatial calibration
technologies are facing the challenges of lack of relevance and real-time. In
this paper, we introduce the pose estimation model and environmental robust
line features extraction to improve the relevance of data fusion and instant
online ability of correction. Dynamic targets eliminating aims to seek optimal
policy considering the correspondence of point cloud matching between adjacent
moments. The searching optimization process aims to provide accurate parameters
with both computation accuracy and efficiency. To demonstrate the benefits of
this method, we evaluate it on the KITTI benchmark with ground truth value. In
online experiments, our approach improves the accuracy by 38.5\% than the soft
synchronization method in temporal calibration. While in spatial calibration,
our approach automatically corrects disturbance errors within 0.4 second and
achieves an accuracy of 0.3-degree. This work can promote the research and
application of sensor fusion.
