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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.
This universal feature matching module takes segmentation masks as inputs and is 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.
Our source code, demo video, and supplementary material are publicly available
at https://mias.group/MIAS-LCEC/.
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