Indiana University-Purdue University Indianapolis (IUPUI) / Obstacle detection, avoidance and path finding for autonomous vehicles requires precise information of the vehicle’s system environment for faultless navigation and decision making. As such vision and depth perception sensors have become an integral part of autonomous vehicles in the current research and development of the autonomous industry. The advancements made in vision sensors such as radars, Light Detection And Ranging (LIDAR) sensors and compact high resolution cameras is encouraging, however individual sensors can be prone to error and misinformation due to environmental factors such as scene illumination, object reflectivity and object transparency. The application of sensor fusion in a system, by the utilization of multiple sensors perceiving similar or relatable information over a network, is implemented to provide a more robust and complete system information and minimize the overall perceived error of the system. 3D LIDAR and monocular camera are the most commonly utilized vision sensors for the implementation of sensor fusion. 3D LIDARs boast a high accuracy and resolution for depth capturing for any given environment and have a broad range of applications such as terrain mapping and 3D reconstruction. Despite 3D LIDAR being the superior sensor for depth, the high cost and sensitivity to its environment make it a poor choice for mid-range application such as autonomous rovers, RC cars and robots. 2D LIDARs are more affordable, easily available and have a wider range of applications than 3D LIDARs, making them the more obvious choice for budget projects. The primary objective of this thesis is to implement a smart and robust sensor fusion system using 2D LIDAR and a stereo depth camera to capture depth and color information of an environment. The depth points generated by the LIDAR are fused with the depth map generated by the stereo camera by a Fuzzy system that implements smart fusion and corrects any gaps in the depth information of the stereo camera. The use of Fuzzy system for sensor fusion of 2D LIDAR and stereo camera is a novel approach to the sensor fusion problem and the output of the fuzzy fusion provides higher depth confidence than the individual sensors provide. In this thesis, we will explore the multiple layers of sensor and data fusion that have been applied to the vision system, both on the camera and lidar data individually and in relation to each other. We will go into detail regarding the development and implementation of fuzzy logic based fusion approach, the fuzzification of input data and the method of selection of the fuzzy system for depth specific fusion for the given vision system and how fuzzy logic can be utilized to provide information which is vastly more reliable than the information provided by the camera and LIDAR separately