  LiDAR-camera extrinsic calibration (LCEC) is crucial for data fusion in
intelligent vehicles. Offline, target-based approaches have long been the
preferred choice in this field. However, they often demonstrate poor
adaptability to real-world environments. This is largely because extrinsic
parameters may change significantly due to moderate shocks or during extended
operations in environments with vibrations. In contrast, online, target-free
approaches provide greater adaptability yet typically lack robustness,
primarily due to the challenges in cross-modal feature matching. Therefore, in
this article, we unleash the full potential of large vision models (LVMs),
which are emerging as a significant trend in the fields of computer vision and
robotics, especially for embodied artificial intelligence, to achieve robust
and accurate online, target-free LCEC across a variety of challenging
scenarios. Our main contributions are threefold: we introduce a novel framework
known as MIAS-LCEC, provide an open-source versatile calibration toolbox with
an interactive visualization interface, and publish three real-world datasets
captured from various indoor and outdoor environments. The cornerstone of our
framework and toolbox is the cross-modal mask matching (C3M) algorithm,
developed based on a state-of-the-art (SoTA) LVM and capable of generating
sufficient and reliable matches. Extensive experiments conducted on these
real-world datasets demonstrate the robustness of our approach and its superior
performance compared to SoTA methods, particularly for the solid-state LiDARs
with super-wide fields of view.
