This visualisation uses AI Generated code, finetuned for the best visualisation, not code quality

Interactive C + Raylib debugger for the extended Kalman filter, exposing predict/update phases, Jacobians, covariance changes, innovation terms, and the geometry of state correction.

• The separation between predict and measurement-update phases
• How nonlinear models are linearized with Jacobians
• How covariance and Kalman gain shape the correction step
• A help-rich debugger layout for stepping through EKF internals

flowchart LR
    A["Prior State"]
    B["Predict Step"]
    C["Predicted Covariance"]
    D["Measurement Innovation"]
    E["Kalman Gain"]
    F["Corrected State"]

    A --> B
    B --> C
    C --> D
    D --> E
    E --> F

• q: quit
• h or ?: toggle help
• The rest of the step, speed, and inspection controls are shown in the app UI

make run