Robotics & simulation
SLAM & Kalman Localization
A state estimate becomes more useful when it knows what it does not know.
Estimate, observe, correct.
Ready to inspect— Ground truth— Kalman estimate· Noisy observations95% covariance ellipse
Observation model
Turn observations off to see uncertainty grow while the filter predicts. Drag in the map or use its arrow keys to steer.
Measured error
Filtered RMS
Observation RMS
Map units. This study isolates 2D localization with a constant-velocity Kalman filter; it is not a complete SLAM implementation.