Do I need to install anything?

No. It runs in any modern browser, free, with no account required.

For Students · Kalman Filter

Kalman Filter for a robot maze

Built for students learning it for a class or exam. See the concept move instead of memorizing formulas — and check your homework intuition. Simulate a robot maze live below — adjust the inputs and watch it respond, right in your browser.

Kalman FilterLive

optimal state estimation

Controls

Presets

Measurement noise

Process noise

The Kalman filter fuses a motion model with noisy measurements to track a hidden state optimally. Each step it predicts, then corrects using the Kalman gain — trusting the measurement more when the model is uncertain, and vice versa. It smooths the jittery sensor (gray) into a clean estimate (cyan) that hugs the truth. The math behind GPS, radar, and spacecraft navigation.

▶ Run in Python

Data Inspector

Measurement RMSE0.0

Kalman RMSE0.0

Noise reductionNaN%

Governing equation

\overset{x}{^} \leftarrow \overset{x}{^} + K (z - H \overset{x}{^}), K = \frac{P H ^{T}}{H P H ^{T} + R}, R = 900, Q = 1.00

Reading this result: Measurement noise dwarfs process noise, so the Kalman gain stays small — the filter leans on its motion model and smooths the jitter hard.

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Frequently asked questions

Is this good for students?: Yes — this version of "Kalman Filter for a robot maze" is framed for students learning it for a class or exam. See the concept move instead of memorizing formulas — and check your homework intuition.
Do I need to install anything?: No. It runs in any modern browser, free, with no account required.