atulhari/Tracking-Navigation-and-SLAM
The exercises are all part of a typical application theme, namely tracking, navigation and SLAM: • Bayesian estimation applied to beacon based measurement systems • Kinematic and dynamic models for tracking • Tracking based on discrete Kalman filtering for linear-Gaussian systems • Tracking with extended Kalman filtering in nonlinear systems • Tracking with particle filtering in nonlinear systems • Slam As such the exercises cover the following theoretical subjects: 1. Fundamentals of parameter estimation; static and scalar case 2. Unbiased linear minimum mean square estimation; static and scalar case 3. Unbiased linear minimum mean square estimation; static and vectorial case 4. Propagation of uncertainty in Gaussian-linear systems; prediction 5. Discrete Kalman filtering 6. Extended Kalman filtering 7. Particle filtering 8. SLAM