Light detection and ranging LiDAR systems onboard mobile platforms are in rapid advancement for real-time mapping applications. Modern 3D laser scanners have a high data rate which, coupled with the complexity of their processing methods, makes simultaneous online localization and mapping (SLAM) a computational challenge. Different 3D LiDAR SLAM algorithms have emerged in recent years, most notably LiDAR Odometry and Mapping and its derivatives. This paper performs the implementation of A-LOAM, ISCLOAM, and LeGO-LOAM algorithms and a respective comparison with the total sequences of the KITTI database which includes different environments and routes from a Velodyne HDL-64E sensor. The results evaluate the performance of the methods on computational cost, absolute error, and relative error. Our code implementation is available online. https://github.com/HaroldMurcia/LOaM-comparison.