Research on online calibration of lidar and camera for intelligent connected vehicles based on depth-edge matching

Abstract

Abstract The practicality of online calibration algorithms in actual autonomous driving scenarios is enhanced by proposing an online calibration method for intelligent networked automotive lidar and camera based on depth-edge matching. The initial values of external parameters are estimated and calculated through hand-eye calibration. The solution of hand-eye calibration is optimized and accurate external parameters are obtained through data conversion. The CMA-ES algorithm is utilized to optimize the optimized parameters which are further compared with the conventional method based on edge matching. It is found that the provided frames of data, the external parameters can be appropriately improved by the method in this paper, and the algorithm congregates in about 1000 seconds. However, the conventional method cannot optimize the parameters correctly when there are only 2 frames of data. The rotation error of most results of this method is between 0.1° and 0.8°, and the translation error is between 0.02m and 0.06m. Compared with other representative algorithms of various methods, the errors in all aspects are more balanced and there is no outstanding error value.