Academic Thesis and Source Code on Robot Localization using Kalman Filter

This document presents an academic thesis and corresponding source code focused on robot localization utilizing Kalman filter technology. The research provides an in-depth exploration of Kalman filter applications in robotic positioning systems, including detailed mathematical modeling of the prediction and correction phases. The implementation features sensor fusion algorithms that integrate data from multiple sources such as GPS, IMU, and wheel encoders to enhance positioning accuracy. The accompanying source code demonstrates practical implementation of both linear and extended Kalman filters (EKF), with modules handling motion models for various robotic platforms. Key functions include state prediction based on kinematic equations, measurement updates incorporating sensor data, and covariance matrix management for error estimation. The code architecture follows modular design principles, separating sensor interfaces, filter implementations, and visualization components. The thesis thoroughly examines critical aspects of robotic localization including motion modeling for different vehicle types, error analysis techniques for system optimization, and performance evaluation metrics. Through systematic experimentation and code examples, the material illustrates how Kalman filtering effectively addresses challenges like sensor noise reduction, drift compensation, and real-time position estimation. This resource serves as valuable reference material for researchers and engineers working in robotics, autonomous systems, and navigation technology development.