State Estimation
Fuse noisy measurements from multiple sensors to maintain an accurate estimate of the robot's state.
Core Topics
Bayesian EstimationKalman Filter (KF)Extended Kalman Filter (EKF)Particle Filters (Monte Carlo Localization)Sensor Fusion
Key Takeaways
- Implement the standard Kalman Filter for linear systems
- Apply Extended Kalman Filters (EKF) to non-linear kinematics
- Develop Particle Filters for global localization
- Handle sensor asynchrony and latency in filtering