Accurate spatiotemporal calibration is crucial for effective data fusion in Visual-Inertial-LiDAR (VIL) systems. However, it remains challenging for setups involving omnidirectional cameras due to their ultra-wide Field-of-View (FoV) and severe distortion. Existing methods often rely on calibration targets or lack native support for such configurations. This letter presents OmniVIL-Calib, a target-free framework for the joint spatiotemporal calibration of an omnidirectional camera, IMU, and LiDAR. We propose an omnidirectional motion-flow model that generalizes the mapping-free paradigm to handle the non-linear geometric distortions inherent in ultra-wide FoV cameras and introduce a robust spherical cross-modal association to align visual and LiDAR edges. These constraints are jointly optimized to estimate all extrinsic parameters simultaneously. Validated on our self-collected OmniVIL dataset, the proposed approach achieves an average accuracy of 0.82 cm in translation and 0.13

$^{\circ }$

in rotation. Experimental results show that our method achieves accuracy comparable to target-based benchmarks and exhibits superior performance compared to existing target-free solutions. To facilitate further research, the OmniVIL dataset and the source code are available at https://github.com/EthanTwo2022/omniVIL_Calib.

Yihan Tu

OmniVIL-Calib: Target-Free Joint Calibration for Omnidirectional Camera, IMU, and LiDAR