Aiming at the problems that the calibration accuracy of light detection and ranging (LiDAR) and camera can greatly affect fusion positioning initialization,and localization algorithms are very sensitive to the accuracy of calibration parameters,which may seriously reduce the performance and reliability of the localization algorithm,the paper proposed a classification method of combined calibration of LiDAR and camera:the latest developments in this field were discussed;and the highlight research achievements of feature-based method,the motion based method,the mutual information based method and deep learning based method were analyzed and summarized emphatically;then,the open source code tools of those four methods were listed,and their features,calibration accuracy and automation level were comparatively analyzed;finally,the future development trends of combined calibration of LiDAR and camera were concluded.
light detection and ranging (LiDAR)camerastereo visioncheckerboardextrinsic calibrationjoint calibration
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