This thesis explores the application of sensor fusion techniques for outdoor localization in autonomous vehicles, with a focus on Kalman filter variations and their optimization for car robots. It examines various algorithms and sensor combinations through experiments in a simulated environment, using the Gazebo simulator and the Robotic Operation System (ROS) framework. The study emphasizes addressing GPS signal loss in challenging environments like urban tunnels, proposing solutions for maintaining accurate location estimates. Additionally, it includes the development of software tools for visualization and data analysis within ROS, and considers hardware compatibility for future physical implementation.