Skip to content

Latest commit

 

History

History
283 lines (197 loc) · 9.92 KB

README.rst

File metadata and controls

283 lines (197 loc) · 9.92 KB
Raw

pynav

Tests

An on-manifold state estimation library for robotics.

The core idea behind this project is to abstract-away the state definition such that a single estimator implementation can operate on a variety of state manifolds, such as the usual vector space, and any Lie group. At the moment, algorithms and features of this package include:

  • Extended Kalman Filter
  • Iterated Extended Kalman Filter
  • Sigmapoint Kalman Filters (Unscented, Spherical Cubature, Gauss-Hermite)
  • Interacting Multiple Model Filter
  • Batch MAP Estimation
  • A large collection of common process and measurement models
  • Out-of-the-box on-manifold numerical jacobian using finite differencing
  • Various utils for plotting, error, and consistency evaluation
  • Monte Carlo experiment executor with result aggregation
  • A preintegration module for linear, wheel odometry, and IMU process models

By implementing a few classes, the user can model almost any problem. Documentation can be found by https://decargroup.github.io/pynav

Setup

Installation

Clone this repo, change to its directory, and execute

pip install -e .

This command should automatically install all dependencies, including our package pylie (found at https://github.com/decargroup/pylie) for back-end Lie group mathematical operations.

Examples

Some starting examples running EKFs can be found in the examples/ folder. Simply run these as python3 scripts

Tests

To run integration tests,

pytest tests

or, to run tests on a specific test file,

pytest -ra tests/integration/filename.py

The Core Concept - Defining a State Implementation

The algorithms and models in this repo are centered around the abstract State class. An instance of State is an object containing, at a minimum, the following attributes:

  • value: a value of some sort;
  • dof: the degrees of freedom associated with the state.

It will also contain the following mandatory methods that must be implemented by the user.

  • plus(): A generic "addition" operation given a dx vector with as many elements as the dof of this state.
  • minus(): A generic "subtraction" operation given another State object of the same type, which returns a numpy array of error values.
  • copy(): A method that returns a new object of the same type, and with the same attibute values.

Optionally, it is often useful to assign a timestamp (stamp) and a label (state_id) to differentiate state instances from others. The snippet below shows how to define a simple vector-space state:

from pynav.types import State
import numpy as np

class VectorState(State):
    """
    A standard vector-based state, with value represented by a 1D numpy array.
    """

    def __init__(self, value: np.ndarray, stamp: float = None, state_id=None):
        super(VectorState, self).__init__(
            value=value,
            dof=value.size,
            stamp=stamp,
            state_id=state_id,
        )

    def plus(self, dx: np.ndarray):
        new = self.copy()
        new.value = new.value + dx
        return new

    def minus(self, x: "VectorState") -> np.ndarray:
        return self.value - x.value

    def copy(self) -> "VectorState":
        return VectorState(self.value.copy(), self.stamp, self.state_id)

As another more complicated example, a state object belonging to the SE(3) Lie group can be implemented as

from pynav.types import State
from pylie import SE3
import numpy as np

class SE3State(State):
    def __init__(self, value: np.ndarray, stamp: float = None, state_id=None):
        super(SE3State, self).__init__(
            value=value,
            dof=6,
            stamp=stamp,
            state_id=state_id,
        )

    def plus(self, dx: np.ndarray):
        new = self.copy()
        new.value = new.value @ SE3.Exp(dx)
        return new

    def minus(self, x: "SE3State") -> np.ndarray:
        return SE3.Log(SE3.inverse(x.value) @ self.value)

    def copy(self) -> "SE3State":
        return SE3State(self.value.copy(), self.stamp, self.state_id)

Process and Measurement Models

System Diagram

System Diagram

There are a few more core types in this package. The main ones are the ProcessModel and MeasurementModel classes. Both of these are abstract classes requiring the user to implement

  • an evaluate() method,
  • a jacobian() method,
  • and a covariance() method.

For example, a simple "single integrator" (velocity input) model can be implemented as follows:

class SingleIntegrator(ProcessModel):
    """
    The single-integrator process model is a process model of the form

        x_k = x_{k-1} + dt * u_{k-1}
    """

    def __init__(self, Q: np.ndarray):
        self._Q = Q

    def evaluate(self, x: VectorState, u: StampedValue, dt: float) -> np.ndarray:
        """
        Returns a state with an updated value according to a process model.
        """
        x.value = x.value + dt * u.value
        return x

    def jacobian(self, x: VectorState, u: StampedValue, dt: float) -> np.ndarray:
        """
        Jacobian of the process model with respect to the state.
        """
        return np.identity(x.dof)

    def covariance(self, x: VectorState, u: StampedValue, dt: float) -> np.ndarray:
        """
        Returns the covariance of the process model errors.
        """
        return dt**2 * self._Q

Similarly, a single distance-to-landmark measurement model can be implemented as

class RangePointToAnchor(MeasurementModel):
    """
    Range measurement from a point state to an anchor (which is also another
    point).
    """

    def __init__(self, anchor_position: List[float], R: float):
        self._r_cw_a = np.array(anchor_position)
        self._R = np.array(R)

    def evaluate(self, x: VectorState) -> np.ndarray:
        r_zw_a = x.value
        y = np.linalg.norm(self._r_cw_a - r_zw_a)
        return y

    def jacobian(self, x: VectorState) -> np.ndarray:
        r_zw_a = x.value
        r_zc_a = r_zw_a - self._r_cw_a
        y = np.linalg.norm(r_zc_a)
        return r_zc_a.reshape((1, -1)) / y

    def covariance(self, x: VectorState) -> np.ndarray:
        return self._R

In fact, for both ProcessModel and MeasurementModel, subclasses will inherit a finite-difference numerical differentiation method jacobian_fd(), that allows for a seamless way to check your jacobian() implementation! (evaluate() method must be implemented for this to work, see some of the files in tests/ for an example of this.)

Built-in Library

Many state, process, and measurement models are already written and part of the built-in library and, as an example, can be accessed with

from pynav.lib.states import VectorState, SE3State
from pynav.lib.models import RangePoseToAnchor, Altitude

The following state types are currently part of the lib:

  • VectorState
  • SO2State
  • SO3State
  • SE2State
  • SE3State
  • SE23State
  • IMUState (contains IMU biases as part of the state)
  • CompositeState (for holding many sub-states as a single state)

The following process models are currently part of the lib:

  • SingleIntegrator
  • BodyFrameVelocity
  • RelativeBodyFrameVelocity
  • CompositeProcessModel
  • IMUKinematics

The following measurement models are currently part of the lib:

  • RangePointToAnchor
  • RangePoseToAnchor
  • RangePoseToPose
  • RangeRelativePose
  • GlobalPosition
  • Altitude
  • Gravitometer
  • and many more

Finally, this repo has the following state estimation algorithms implemented:

  • ExtendedKalmanFilter
  • IteratedKalmanFilter
  • UnscentedKalmanFilter
  • InteractingModelFilter
  • and more

Contributing

If you wish to make some changes, create a branch, make your changes, and then make a pull request. Here are some conventions that should be followed:

  • Code style should follow the PEP8 style guide. https://peps.python.org/pep-0008
  • Everything should be type hinted as much as possible. Essentially, in the VS Code dark theme, you should not have any white text anywhere.

The goal of this project is to write general algorithms that work for any implementation of the abstract State, ProcessModel and MeasurementModel. As such, please give thought to how this could be done to any algorithm you implement.

Contributing to the Documentation

The documentation is automatically generated from python docstrings using sphinx, which can be installed by following these instructions: https://www.sphinx-doc.org/en/master/usage/installation.html.

After sphinx is installed change to the ./docs/ directory and run

make html

and the documentation will be updated (you may need to install some dependencies... sorry. just follow the error prompts and install whats required. TODO). In terms of actually writing documentation, we use the numpy format, which can be seen in some of the existing docstrings in the code, and can be used as a template.

Alternatively and prefereably, install the autoDocstring extension for VSCode: https://marketplace.visualstudio.com/items?itemName=njpwerner.autodocstring and change the docstring format in the settings to numpy.