How do we estimate internal, hidden state variables through noisy measurement data?
The solution to state estimation is related to the specific Motion Equation and Observation Equation of a robot and the noise probability distribution. Based on the motion/observation equations, and whether the noise is Gaussian, state estimation is divided into linear/non-linear and Gaussian/non-Gaussian systems.
Notes from: Kalman Filter from The Ground Up by Alex Becker
Basic State Estimation
- Simple Static State Estimation Example
- Constant Velocity Tracking Example
- Constant Acceleration Tracking Example
Kalman Filtering
Kalman Filtering Basics
- Kalman Filter
- 1D Kalman Filter without Process Noise
- Kalman Gain Intuition
- 1D Kalman Filter with Process Noise