#kalman-filter #fixed-point #slam #embedded

no-std minikalman

A microcontroller targeted Kalman filter implementation

10 releases (6 breaking)

0.6.0 Jun 22, 2024
0.5.0 Jun 20, 2024
0.4.0 Jun 7, 2024
0.3.0 Jun 3, 2024
0.0.2 Jun 11, 2023

#5 in Robotics

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MIT license

615KB
12K SLoC

Kalman Filters for Embedded Targets (in Rust)

Crates.io Crates.io GitHub Workflow Status docs.rs codecov

This is the Rust port of my kalman-clib library, a microcontroller targeted Kalman filter implementation, as well as the libfixkalman C library for Q16.16 fixed-point Kalman filters. It optionally uses micromath for square root calculations on no_std, and can use libm is wished for. Depending on the configuration, this crate may require f32 / FPU support.

This implementation uses statically allocated buffers for all matrix operations. Due to lack of const generics for array allocations in Rust, this crate also provides helper macros to create the required arrays.

Kalman Filter Library Hero Picture

no_std vs std, alloc

This crate builds as no_std by default. To build with std support, run:

cargo build --features=std

Independently of std you can turn on alloc features. This enables simplified builders with heap-allocated buffers:

cargo build --features=alloc

Examples

Targets with allocations (std or alloc)

When the alloc crate feature is enabled either directly or implicitly via std, some builders are enabled that allow for simple creation of filters. This should help non-embedded use cases, or any use case that does not have to explicitly manage buffer locations, to get an easier start:

const NUM_STATES: usize = 3;
const NUM_CONTROLS: usize = 2;
const NUM_OBSERVATIONS: usize = 1;

fn example() {
    let builder = regular::builder::KalmanFilterBuilder::<NUM_STATES, f32>::default();
    let mut filter = builder.build();
    let mut control = builder.controls().build::<NUM_CONTROLS>();
    let mut measurement = builder.observations().build::<NUM_OBSERVATIONS>();

    // Set up the system dynamics, control matrices, observation matrices, ...

    // Filter!
    loop {
        // Update your control vector(s).
        control.control_vector_mut().apply(|u| {
            u[0] = 0.0;
            u[1] = 1.0;
        });

        // Update your measurement vectors.
        measurement.measurement_vector_mut().apply(|z| {
            z[0] = 42.0;
        });

        // Update prediction (without controls).
        filter.predict();

        // Apply any controls to the prediction.
        filter.control(&mut control);

        // Apply any measurements.
        filter.correct(&mut measurement);

        // Access the state
        let state = filter.state_vector();
        let covariance = filter.system_covariance();
    }
}

Extended Kalman Filters

The general setup remains the same, however the predict and correct methods are replaced with their nonlinear counterparts:

const NUM_STATES: usize = 3;
const NUM_OBSERVATIONS: usize = 1;

fn example() {
    let builder = extended::builder::KalmanFilterBuilder::<NUM_STATES, f32>::default();
    let mut filter = builder.build();
    let mut measurement = builder.observations().build::<NUM_OBSERVATIONS>();

    // The time step of our simulation.
    const DELTA_T: f32 = 0.1;

    // Set up the initial state vector.
    filter.state_vector_mut().apply(|vec| {
        vec.set_row(0, 0.0);
        vec.set_row(1, 0.0);
        vec.set_row(2, 1.0);
        vec.set_row(3, 1.0);
    });

    // Set up the initial estimate covariance as an identity matrix.
    filter.estimate_covariance_mut().make_identity();

    // Set up the process noise covariance matrix as an identity matrix.
    measurement
        .measurement_noise_covariance_mut()
        .make_scalar(1.0);

    // Set up the measurement noise covariance.
    measurement.measurement_noise_covariance_mut().apply(|mat| {
        mat.set_value(1.0); // matrix is 1x1
    });

    // Simulate
    for step in 1..=100 {
        let time = step as f32 * DELTA_T;

        // Update the system transition Jacobian matrix.
        filter.state_transition_mut().apply(|mat| {
            mat.make_identity();
            mat.set(0, 2, DELTA_T);
            mat.set(1, 3, DELTA_T);
        });

        // Perform a nonlinear prediction step.
        filter.predict_nonlinear(|state, next| {
            // Simple constant velocity model.
            next[0] = state[0] + state[2] * DELTA_T;
            next[1] = state[1] + state[3] * DELTA_T;
            next[2] = state[2];
            next[3] = state[3];
        });

        // Prepare a measurement.
        measurement.measurement_vector_mut().apply(|vec| {
            // Noise setup.
            let mut rng = rand::thread_rng();
            let measurement_noise = Normal::new(0.0, 0.5).unwrap();

            // Perform a noisy measurement of the (simulated) position.
            let z = (time.powi(2) + time.powi(2)).sqrt();
            let noise = measurement_noise.sample(&mut rng);

            vec.set_value(z + noise);
        });

        // Update the observation Jacobian.
        measurement.observation_matrix_mut().apply(|mat| {
            let x = filter.state_vector().get_row(0);
            let y = filter.state_vector().get_row(1);

            let norm = (x.powi(2) + y.powi(2)).sqrt();
            let dx = x / norm;
            let dy = y / norm;

            mat.set_col(0, dx);
            mat.set_col(1, dy);
            mat.set_col(2, 0.0);
            mat.set_col(3, 0.0);
        });

        // Apply nonlinear correction step.
        filter.correct_nonlinear(&mut measurement, |state, observation| {
            // Transform the state into an observation.
            let x = state.get_row(0);
            let y = state.get_row(1);
            let z = (x.powi(2) + y.powi(2)).sqrt();
            observation.set_value(z);
        });
    }
}

For a slightly more realistic EKF example that simulates radar measurements of a moving object, see the radar-2d example.

cargo run --example radar-2d --features=std

Embedded Targets

An example for STM32F303 microcontrollers can be found in the xbuild-tests/stm32 directory. It showcases both fixed-point and floating-point support.

Q16.16 fixed-point

Run the fixed example with the fixed crate feature. This enables I16F16 type support, similar to the libfixkalman C library.

cargo run --example fixed --features=fixed

Gravity Constant Estimation Example

To run the example gravity simulation, run either

cargo run --example gravity --features=std
cargo run --example gravity --features=std,libm

This will estimate the (earth's) gravitational constant (g ≈ 9.807 m/s²) through observation of the position of a free-falling object. When executed, it should print something along the lines of:

At t = 0, predicted state: s = 3 m, v = 6 m/s, a = 6 m/s²
At t = 0, measurement: s = 0 m, noise ε = 0.13442 m
At t = 0, corrected state: s = 0.908901 m, v = 3.6765568 m/s, a = 5.225519 m/s²
At t = 1, predicted state: s = 7.1982174 m, v = 8.902076 m/s, a = 5.225519 m/s²
At t = 1, measurement: s = 4.905 m, noise ε = 0.45847 m
At t = 1, corrected state: s = 5.6328573 m, v = 7.47505 m/s, a = 4.5993752 m/s²
At t = 2, predicted state: s = 15.407595 m, v = 12.074425 m/s, a = 4.5993752 m/s²
At t = 2, measurement: s = 19.62 m, noise ε = -0.56471 m
At t = 2, corrected state: s = 18.50683 m, v = 14.712257 m/s, a = 5.652767 m/s²
At t = 3, predicted state: s = 36.04547 m, v = 20.365025 m/s, a = 5.652767 m/s²
At t = 3, measurement: s = 44.145 m, noise ε = 0.21554 m
At t = 3, corrected state: s = 42.8691 m, v = 25.476515 m/s, a = 7.3506646 m/s²
At t = 4, predicted state: s = 72.02094 m, v = 32.82718 m/s, a = 7.3506646 m/s²
At t = 4, measurement: s = 78.48 m, noise ε = 0.079691 m
At t = 4, corrected state: s = 77.09399 m, v = 36.10087 m/s, a = 8.258889 m/s²
At t = 5, predicted state: s = 117.3243 m, v = 44.359756 m/s, a = 8.258889 m/s²
At t = 5, measurement: s = 122.63 m, noise ε = -0.32692 m
At t = 5, corrected state: s = 120.94025 m, v = 46.38022 m/s, a = 8.736543 m/s²
At t = 6, predicted state: s = 171.68874 m, v = 55.11676 m/s, a = 8.736543 m/s²
At t = 6, measurement: s = 176.58 m, noise ε = -0.1084 m
At t = 6, corrected state: s = 174.93135 m, v = 56.704926 m/s, a = 9.062785 m/s²
At t = 7, predicted state: s = 236.16766 m, v = 65.76771 m/s, a = 9.062785 m/s²
At t = 7, measurement: s = 240.35 m, noise ε = 0.085656 m
At t = 7, corrected state: s = 238.87048 m, v = 66.942894 m/s, a = 9.276019 m/s²
At t = 8, predicted state: s = 310.4514 m, v = 76.21891 m/s, a = 9.276019 m/s²
At t = 8, measurement: s = 313.92 m, noise ε = 0.8946 m
At t = 8, corrected state: s = 313.03793 m, v = 77.22877 m/s, a = 9.44006 m/s²
At t = 9, predicted state: s = 394.98672 m, v = 86.66882 m/s, a = 9.44006 m/s²
At t = 9, measurement: s = 397.31 m, noise ε = 0.69236 m
At t = 9, corrected state: s = 396.6648 m, v = 87.26297 m/s, a = 9.527418 m/s²
At t = 10, predicted state: s = 488.69147 m, v = 96.79039 m/s, a = 9.527418 m/s²
At t = 10, measurement: s = 490.5 m, noise ε = -0.33747 m
At t = 10, corrected state: s = 489.46213 m, v = 97.03994 m/s, a = 9.560934 m/s²
At t = 11, predicted state: s = 591.28253 m, v = 106.600876 m/s, a = 9.560934 m/s²
At t = 11, measurement: s = 593.51 m, noise ε = 0.75873 m
At t = 11, corrected state: s = 592.75964 m, v = 107.04147 m/s, a = 9.615404 m/s²
At t = 12, predicted state: s = 704.6088 m, v = 116.656876 m/s, a = 9.615404 m/s²
At t = 12, measurement: s = 706.32 m, noise ε = 0.18135 m
At t = 12, corrected state: s = 705.4952 m, v = 116.90193 m/s, a = 9.643473 m/s²
At t = 13, predicted state: s = 827.2188 m, v = 126.5454 m/s, a = 9.643473 m/s²
At t = 13, measurement: s = 828.94 m, noise ε = -0.015764 m
At t = 13, corrected state: s = 827.97705 m, v = 126.74077 m/s, a = 9.66432 m/s²
At t = 14, predicted state: s = 959.55 m, v = 136.40509 m/s, a = 9.66432 m/s²
At t = 14, measurement: s = 961.38 m, noise ε = 0.17869 m
At t = 14, corrected state: s = 960.39984 m, v = 136.6101 m/s, a = 9.684802 m/

Dependencies

~0.5–1.7MB
~33K SLoC