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Unlocking Wide-FoV Perception: A Robust Targetless Sensor Calibration Framework for Fisheye–LiDAR Fusion
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DOI：10.1109/tii.2026.3683378.png)
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Abstract
En 中文
Driven by the increasing accessibility of high-performance sensors, 3-D scene reconstruction is transitioning from vision-centric neural representations toward multimodal LiDAR–camera fusion to ensure absolute metric consistency. However, a critical structural bottleneck remains: existing LiDAR–camera calibration (LCC) methods rarely prioritize hardware scalability and field-of-view compatibility. Although fisheye lenses provide optimal data utilization for 360<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> scanning LiDARs, their severe optical distortion disrupts traditional cross-modal alignment, causing conventional geometric and statistical paradigms to fail. To bridge this gap, we propose a robust, targetless calibration framework specifically tailored for wide-FoV Fisheye–LiDAR systems. Our approach introduces a cross-modal alignment strategy that leverages LiDAR low-reflectivity edge priors and adaptive distortion modeling, achieving precise 2D-to-3D registration without physical targets. The accuracy of the method is demonstrated through extensive experiments and further validated via dynamic scene reconstruction using the FAST-LIVO2 system. The results indicate that this framework provides a reliable, on-the-fly LCC method to support omnidirectional perception hardware in practical applications.
Keywords:
3-D reconstruction
calibration
fisheye-LiDAR fusion
targetless
wide field of view (FoV)
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