To address the inaccurate extraction of 3D point cloud of the calibration plate encountered in the process of external parameter calibration of lidar in complex external environment, an optimization method of external parameter calibration of lidar and camera based on background clustering is proposed. The blind detection of the point cloud of the calibration plate is avoided in the whole threedimensional point cloud, which would lead to large error in the calibration results and the need to manually correct the wrong feature points. This method uses the density difference between the background point cloud without calibration plate and the target point cloud with calibration plate in some spatial domains, and obtains the difference coefficient K between the point cloud of calibration plate and the background point cloud through the adaptive spatial threshold model. Then, some three-dimensional points in the two-point cloud are clustered to complete the three-dimensional point cloud extraction of the calibration plate. Experimental results show that this method can accurately and efficiently extract 3D point cloud of calibration plate in complex environment to improve the accuracy of laser radar and camera external parameter calibration. On this basis, the correct projection proportion of point cloud can reach 97. 43% , and the projection error is reduced by about 25. 33% compared with other methods.