Many robotic systems employ multiple 2-D LiDARs for tasks such as localization and 3-D mapping of expansive environments. Achieving accurate extrinsic calibration between these 2-D LiDARs is essential for effective collaboration and data fusion. This article introduces a novel 2-D LiDAR calibration method that eliminates the need for special artificial markers or the requirement of a mobile platform to scan a wide range of environmental features. In this method, a fixed plane and the attitude information of the mobile platform are leveraged to compute the transformation relationship between multiple 2-D LiDARs. Further enhancing the accuracy of the calibration results, nonlinear optimization methods are employed. The LiDAR calibration effectiveness of the proposed algorithm is evaluated through simulation studies. To validate its real-world performance, experiments are conducted and compared with state-of-the-art LiDAR calibration algorithms, demonstrating calibration results comparable to the best in the field. Furthermore, the calibrated LiDARs enable the creation of highly satisfactory 3-D maps of exterior wall structures, showcasing the practical utility and impact of this calibration method in real-world applications.