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Monocular depth-assisted joint optimization calibration of LiDAR-camera extrinsics, moving beyond grayscale mutual information, with superior accuracy and robustness under complex lighting and materials

A two-stage joint optimization calibration method for LiDAR-camera extrinsics assisted by monocular depth is proposed, replacing traditional grayscale-intensity mutual information with depth constraints to improve calibration accuracy and robustness under complex lighting and material conditions. In the coarse calibration stage, a composite calibration board combining ArUco markers and a circular-hole array is used, and an SVD-optimized ICP algorithm solves for initial extrinsic values analytically without requiring preset initial values; in the fine calibration stage, the DepthPro monocular depth estimation network is introduced to construct a joint optimization objective function combining geometric constraints and normalized mutual information distance (NID).

Part of the event: Proposing a monocular depth-assisted two-stage LiDAR-camera calibration optimization method

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