  This paper presents a novel semantic-based online extrinsic calibration
approach, SOIC (so, I see), for Light Detection and Ranging (LiDAR) and camera
sensors. Previous online calibration methods usually need prior knowledge of
rough initial values for optimization. The proposed approach removes this
limitation by converting the initialization problem to a Perspective-n-Point
(PnP) problem with the introduction of semantic centroids (SCs). The
closed-form solution of this PnP problem has been well researched and can be
found with existing PnP methods. Since the semantic centroid of the point cloud
usually does not accurately match with that of the corresponding image, the
accuracy of parameters are not improved even after a nonlinear refinement
process. Thus, a cost function based on the constraint of the correspondence
between semantic elements from both point cloud and image data is formulated.
Subsequently, optimal extrinsic parameters are estimated by minimizing the cost
function. We evaluate the proposed method either with GT or predicted semantics
on KITTI dataset. Experimental results and comparisons with the baseline method
verify the feasibility of the initialization strategy and the accuracy of the
calibration approach. In addition, we release the source code at
https://github.com/--/SOIC.
