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As part of the Electrical and Computer Engineering Senior Design course, this project focuses on developing the localization subsystem for a level 5 autonomous ground vehicle competing in the 2026 Intelligent Ground Vehicle Competition (IGVC) AutoNav Challenge. Accurate localization is essential for determining the vehicle’s position and orientation as it navigates through the competition field. Reliable performance must be maintained despite variable lighting, uneven terrain, and inconsistent satellite signals. The design process began with a needs–metrics matrix derived from competition and customer requirements. These metrics guided the definition of system objectives such as accuracy, reliability, and real-time responsiveness. To achieve these goals, several localization methods have been implemented including the position and velocity of the Global Positioning System (GPS), wheel encoders, and inertial measurement sensors (IMU). Data from these sensors was fused using estimation algorithms to minimize drift and maintain stability. The two main algorithms that have been implemented are Extended Kalman Filters (EKF), and Simultaneous Localization and Mapping (SLAM). Each offers advantages in handling uncertainty, environmental mapping, and computational efficiency. This course-based research highlights the ideation and engineering design process; translating requirements into measurable goals and identifying algorithmic solutions that enable safer and more reliable autonomous navigation.