  This paper presents a framework for the targetless extrinsic calibration of
stereo cameras and Light Detection and Ranging (LiDAR) sensors with a
non-overlapping Field of View (FOV). In order to solve the extrinsic
calibrations problem under such challenging configuration, the proposed
solution exploits road markings as static and robust features among the various
dynamic objects that are present in urban environment. First, this study
utilizes road markings that are commonly captured by the two sensor modalities
to select informative images for estimating the extrinsic parameters. In order
to accomplish stable optimization, multiple cost functions are defined,
including Normalized Information Distance (NID), edge alignment and, plane
fitting cost. Therefore a smooth cost curve is formed for global optimization
to prevent convergence to the local optimal point. We further evaluate each
cost function by examining parameter sensitivity near the optimal point.
Another key characteristic of extrinsic calibration, repeatability, is analyzed
by conducting the proposed method multiple times with varying randomly
perturbed initial points.
