Environment-Driven Online LiDAR-Camera Extrinsic Calibration

• URL: http://arxiv.org/abs/2502.00801v3
• Date: Sun, 09 Nov 2025 15:10:08 GMT
• Title: Environment-Driven Online LiDAR-Camera Extrinsic Calibration
• Authors: Zhiwei Huang, Jiaqi Li, Hongbo Zhao, Xiao Ma, Ping Zhong, Xiaohu Zhou, Wei Ye, Rui Fan,
• Abstract summary: We present EdO-LCEC, the first environment-driven online calibration approach.<n>Unlike traditional target-free methods, EdO-LCEC employs a generalizable scene discriminator to estimate the feature density of the application environment.<n>To overcome the challenges of cross-modal feature matching between LiDAR and camera, we introduce dual-path correspondence matching.
• Score: 19.715280035570707
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LiDAR-camera extrinsic calibration (LCEC) is crucial for multi-modal data fusion in autonomous robotic systems. Existing methods, whether target-based or target-free, typically rely on customized calibration targets or fixed scene types, which limit their applicability in real-world scenarios. To address these challenges, we present EdO-LCEC, the first environment-driven online calibration approach. Unlike traditional target-free methods, EdO-LCEC employs a generalizable scene discriminator to estimate the feature density of the application environment. Guided by this feature density, EdO-LCEC extracts LiDAR intensity and depth features from varying perspectives to achieve higher calibration accuracy. To overcome the challenges of cross-modal feature matching between LiDAR and camera, we introduce dual-path correspondence matching (DPCM), which leverages both structural and textural consistency for reliable 3D-2D correspondences. Furthermore, we formulate the calibration process as a joint optimization problem that integrates global constraints across multiple views and scenes, thereby enhancing overall accuracy. Extensive experiments on real-world datasets demonstrate that EdO-LCEC outperforms state-of-the-art methods, particularly in scenarios involving sparse point clouds or partially overlapping sensor views.

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