State Estimation and Localization for Autonomous Vehicles

To navigate safely, an autonomous vehicle must know exactly where it is in the world down to the centimeter. Understanding how self-driving cars process raw sensor data to estimate their position and orientation is a foundational skill for any aspiring robotics engineer. In this text-based course, you will transition from understanding basic physics concepts to grasping the core mathematics and algorithms that power modern autonomous vehicle localization. You will study how vehicles combine data from GNSS, inertial measurement units (IMUs), and wheel odometry to build a robust, real-time estimation system. What you'll learn: - Understand the fundamental kinematics and coordinate frames used in vehicle motion modeling. - Apply least squares estimation and Kalman filtering to process noisy sensor measurements. - Implement sensor fusion techniques that combine GNSS, IMU, and odometry data. - Explore modern state estimation paradigms, including Extended Kalman Filters (EKF) and error-state formulations. - Analyze robust estimation methods and outlier rejection to handle sensor anomalies and signal dropouts. The course begins with foundational concepts in kinematics, probability, and coordinate systems before advancing to recursive state estimation, Kalman filtering, and modern multi-sensor fusion strategies. This course is designed for beginners interested in robotics and autonomous systems, requiring only basic linear algebra and general programming familiarity. No prior experience with self-driving hardware is required. Start learning the algorithmic core of autonomous vehicle localization today.