To address the issues of low registration efficiency, prone to false matching, and difficulty in processing complex point cloud data in the data registration stage of point clouds obtained through lidar scanning, a point cloud registration method is proposed based on the optimization of the sampling rate and the improvement of descriptor extracting. Firstly, the optimal sampling rate for extracting key points is sought based on the influence of the sampling rate on key point extracting, and the voxel filtering method is improved to achieve optimal down-sampling. This ensures the registration accuracy while achieving data streamlining. Secondly, considering the inefficiency of constructing descriptors for global data for corresponding point matching, the key point extraction strategy is utilized to achieve a simplified construction of descriptors and improve the efficiency of corresponding point matching. Then, dual checks are introduced to reduce the probability of false corresponding point matching. Finally, comparative registration experiments are conducted using the actual measurement data of lidar and open-source point cloud datasets. The experimental results indicate that the proposed registration method has higher registration speed while keeping the matching accuracy of point clouds.