Blackwell

Blackwell is a compact, JAX-native probabilistic-robotics library. It provides manifold-aware Gaussian beliefs, weighted particles, Euclidean, SE(2) and SE(3) state spaces, an extended Kalman filter, a bootstrap particle filter, reproducible simulation and uncertainty metrics.

It is useful today for planar localisation, 3D transform uncertainty, linear state estimation and estimator research, while remaining deliberately pre-alpha: expect API changes before version 1.0.

Documentation · Five-minute localisation · Examples · API reference

Explore the documentation for installation guidance, practical tutorials, runnable examples and the complete API reference.

Install

Blackwell requires Python 3.11 or newer. Install the latest release from PyPI:

python -m pip install blackwell

Accelerator-specific JAX packages are intentionally not pinned. See the installation guide for GPU/TPU and development setups.

A first SE(2) estimate

import jax
import jax.numpy as jnp

from blackwell import GaussianBelief
from blackwell.filters.ekf import ExtendedKalmanFilter
from blackwell.models import range_bearing
from blackwell.models import se2 as se2_models
from blackwell.spaces import se2

filter_ = ExtendedKalmanFilter(se2, se2_models, range_bearing)
dynamics = se2_models.BodyMotion(
    process_covariance=jnp.diag(jnp.array([0.01, 0.01, 0.0025]))
)
observation = range_bearing.KnownLandmarksRangeBearing(
    landmarks=jnp.array([[5.0, 0.0], [0.0, 5.0]]),
    measurement_covariance=jnp.diag(jnp.array([0.08, 0.02])),
)
belief = GaussianBelief(
    mean=jnp.array([0.0, 0.0, 0.0]),
    covariance=jnp.diag(jnp.array([0.5, 0.5, 0.1])),
)

belief = jax.jit(filter_.step)(
    belief,
    dynamics,
    observation,
    jnp.array([0.3, 0.0, 0.04]),
    range_bearing.observe(jnp.array([0.35, 0.08, 0.05]), observation),
)
print(belief.mean)

SE(2) states are [x, y, heading]; controls and covariance use local body-frame tangent coordinates [forward, lateral, turn].

SE(3) states are [x, y, z, qx, qy, qz, qw], with a scalar-last Hamilton quaternion. Their covariance uses six body-frame coordinates: translation rho followed by rotation vector phi. See the SE(3) uncertainty example for composition, inversion and point transformation with correlated inputs.

What is included?

• Euclidean and right-retraction SE(2)/SE(3) state spaces
• Linear dynamics and observations
• SE(2) body-motion and known-landmark range-bearing models
• Manifold-aware extended Kalman filtering
• Bootstrap particle filtering with explicit ESS and systematic resampling
• Reproducible trajectory simulation
• RMSE, planar position RMSE and NEES metrics
• Runnable linear, SE(2) EKF, particle-localisation and correlated SE(3) examples

Blackwell does not yet include built-in IMU models, smoothing, SLAM state augmentation, data association, sensor drivers or production persistence. See Choose an estimator for the current fit and limits.

Design commitments

• JAX is the sole numerical backend.
• Mathematical kernels are pure and beliefs are immutable PyTrees.
• Manifold uncertainty lives in local tangent coordinates.
• Geometry, stochastic models and inference remain separate.
• Random keys, JIT compilation and resampling policy stay explicit.
• Simulation and consistency metrics are part of the public API.

Run the examples

git clone https://github.com/unswei/blackwell.git
cd blackwell
uv sync --all-extras
uv run python examples/quickstart.py
uv run python examples/linear_kalman_filter.py
uv run python examples/se3_uncertainty.py
uv run python examples/se2_localisation.py --plot localisation.png
uv run python examples/particle_localisation.py --plot particles.png

Contribute

Set up the reproducible development environment and run all local checks:

uv sync --all-extras
uv run ruff check .
uv run pytest
uv run python -m build
uv run mkdocs build --strict

See CONTRIBUTING.md for workflow, test and documentation expectations.

Licence

Blackwell is licensed under the Apache License 2.0.