# 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

- Source: 3D视觉工坊 · 微信公众号
- Published: 2026-10-16T16:00:00.000Z
- RoboHot: https://robohot.news/en/items/prjy64v3whp9574qh4y3rfcgv
- Original: https://mp.weixin.qq.com/s?__biz=MzU1MjY4MTA1MQ==&mid=2247752241&idx=2&sn=0cb6e6282e0a34a5657844aa5bfff635&chksm=fa5a81cc89f766aa3cd2ba05c4dc1ba51666944fb25f9d1c122cc78873423c6bac10a42d7473&scene=126&sessionid=1792241050#rd

## Summary

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).