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In recent years, studies on autonomous vehicles have been progressively increasing and especially, intensive works have been carried out in many areas like obstacle detection, mapping, localization, path planning, navigation, and obstacle avoidance. In this study, it was aimed to develop a new 3D LIDAR system with much lower cost, portable and 360-degree field of view by using standard 2D laser scanner instead of high-costing and large dimension 3D LIDARs which contribute seriously to the autonomy of the vehicle. The acquisition of a new 3D LIDAR system is mainly based on the rotation of the 2D laser scanner about certain axis and the conversion of the distance data published by this laser scanner into point cloud data, which is called as the 3D Cartesian point cluster. When the 3D LIDAR system is examined as a whole, it needs to communicate in coordinately the components it contains in order to operate at the desired efficiency. Therefore, in order to manage this software and hardware communication, ROS (Robot Operating System), which is a software platform that can be controlled by the computer, was used. In addition, a mobile vehicle was designed to be gained motion feature to the system. The 3D LIDAR platform was integrated into the designed mobile vehicle and the new system was made ready for navigation. For navigation, a number of algorithm and ROS nodes, called Navigation Stack, were used to autonomously move the vehicle from one point to another and avoid all obstacles that may occur during movement. Experiments show that the developed 3D LIDAR system provides an economical and effective solution for all directional object detection in autonomous vehicles. The navigation tests also show that the developed system is successful in performing tasks such as obstacle detection and obstacle avoidance in indoor environments. [ABSTRACT FROM AUTHOR]