AHRS: Attitude and Heading Reference Systems¶
Welcome to AHRS!
ahrs is an open source Python toolbox for attitude
estimation using the most known algorithms, methods and resources.
It is designed flexible and very simple to use, making it a great option for fast prototyping and integration with your own Python projects.
This package is a collection of functions and utilities to help you understand and use the most common techniques for attitude estimation, and in no way it is recommende to be used commercially.
All algorithms and implementations have their proper documentation and references, in case you need further clarification of their usage.
New in version 0.3¶
- The World Magnetic Model (WMM) is fully implemented. It can be used to estimate all magnetic field elements on any given place of Earth for dates between 2015 and 2025.
- The ellipsoid model of the World Geodetic System (WGS84) is included. A full implementation of the Earth Gravitational Model (EGM2008) is NOT available here, but the estimations of the main and derived parameters of the WGS84 using the ellipsoid model are carried out.
- The International Gravity Formula and the EU’s WELMEC normal gravity reference system are also implemented.
- New class
numpy.ndarray) for orientation/rotation representations as 3x3 Direction Cosine Matrices.
- New class
QuaternionArrayto simultaneously handle an array with more quaternions at once.
- New submodule
framesto represent the position of an object in different reference frames.
- Metrics for rotations in 3D spaces using quaternions and direction cosine matrices.
- New operations, properties and methods for class
Quaternion, now also derived from
- A whole bunch of new constant values (mainly for Geodesy) accessed from the top level of the package.
- Docstrings are improved with further explanations, references and equations whenever possible.
- New and improved estimators include a short, but clear documentation with references. Many use different sensor arrays. The available algorithms are:
plot are dismissed, removing dependecies on Scipy and
matplotlib. This decision was made with the intent to better focus on the
algorithmic part of the package.
Loading and visualizing the data is left to the preference of the user.