Autonomous Vehicles (AVs) technology has increased dramatically over the last years. Accurate 3D point cloud maps are needed to perform this autonomous driving task. Mapping allows AVs to adapt to the surrounding environment and maneuver in complex situations. In this paper, a methodology for performance analysis of the LiDAR Inertia Odometry via Smoothing and Mapping (LIO-SAM) framework for the generation of 3D point cloud maps is proposed. The test scenario is intended for outdoor autonomous vehicles making use of an inertial measurement unit (IMU) sensor, a LiDAR sensor and a Global Navigation Satellite System (GNSS) module. The route selected for the collection of the data set is located in the city of Cuenca-Ecuador in the Ecuadorian Andes at an altitude of 2581 m a.s.l. The LIO-SAM framework was meticulously evaluated for accurate real-time vehicle trajectory estimation and mapping. The results obtained were highly precise with a Root Mean Squared Error (RMSE) of 0.04 m, 0.09 m and 0.08 m for the elevation, northing and easting, respectively. This results show a few centimeters of difference between the control points evaluated with cartographic precision equipment and Real-Time Kinematic (RTK) services versus LIO-SAM framework.