Environment mapping and localization is one of the remarkable technology for an autonomous vehicle (AV). Self localization of vehicle or robots with very high accuracy is required for the proper navigation. Through the Simultaneous Localization and Mapping (SLAM), an AV or robot can create a map of its surroundings and simultaneously localize in it. The built maps enable important tasks such as path planning and obstacle avoidance. The Localization and Mapping process should be faster for real time applications. We have validated the accuracy of localization process with ground truth on kitti odometry benchmark dataset. Analysis of the localization accuracy, computational efficiency, rotational and translational error and loop closure has been done. After the evaluation of method, the algorithm is tested on the real time data using velodyne VLP32-C LiDAR. We have optimized the computational time of LiDAR Odometry and Mapping (LOAM) algorithm in order to use it in real time. We have created the 3D point cloud map of our campus and able to simultaneously localize the vehicle on it by our method Real Time LiDAR Odometry and Mapping (RT-LOAM) with centimeter-level accuracy. Also, the annotation on the 3D point cloud map is done to construct the vector map which is compatible to Advanced Driver Assistance Systems (ADAS) map. © 2022 IEEE.