repository·master·Indexed 20 days ago
https://github.com/mayitzin/ahrsA pure Python collection of functions and algorithms for estimating the orientation (attitude) of mobile systems using sensor data. It includes a wide range of attitude estimators based on Wahba's Problem (WP) or Dead Reckoning (DR), such as Madgwick, Mahony, EKF, and QUEST. The library provides tools for handling Rotation Matrices (DCM) and Quaternions, and includes a sensors submodule for generating synthetic Magnetic, Angular Rate, and Gravity (MARG) data for prototyping and testing.
ahrs is an open source Python toolbox designed for attitude estimation. It provides implementations of well-known algorithms, methods, and resources for fast prototyping, testing, and integration into Python projects. The library is intended for educational and research purposes and is not recommended for commercial use.The ahrs.utils.geodesy module provides tools for geodetic calculations. The library includes implementations for specific ellipsoids, magnetic models, and historical geodesy methods.
Key components include:
refEllipsoid).wmm).wgs84.igf and welmec models.The ahrs.utils.metrics module provides metrics for evaluating errors in the Euclidean space R(3), typically used for comparing position or translation vectors. Available metrics include:
euclideanrmsermse_matricesIn AHRS, an attitude describes the orientation of a frame relative to a reference frame. The library provides several mathematical representations to model these orientations, primarily focusing on:
Depending on your application, you can use specific classes to manage these representations and specialized functions to transform or redefine rotations.
Version 0.4 introduced several significant updates:
sensors and geodesy.The ahrs.utils.metrics module provides various metrics for evaluating attitude estimation errors in the Special Orthogonal group SO(3). These metrics are used to compare an estimated attitude against a ground truth. Available metrics include:
chordalidentity_deviationangular_distanceqdistqeipqcipqadAHRS uses several standard coordinate frames to define position and orientation. Understanding these is critical for interpreting sensor data and estimator outputs:
Local frames are used for navigation relative to a local origin (often the sensor location). Common LLFs include:
Quaternion and QuaternionArray classes are derived from numpy.ndarray. This means you can use them directly as regular NumPy arrays while still accessing specialized AHRS methods and properties.ahrs.utils.sensors.Sensors class provides a unified and flexible way to handle common sensors used in attitude estimation algorithms. Instead of managing individual sensor data streams manually, you can use this class to encapsulate the most frequent sensor types required for AHRS (Attitude and Heading Reference Systems) tasks.AHRS provides dedicated classes to handle different mathematical representations of attitude. You can instantiate these classes to perform rotations and coordinate transformations:
classDCM: For working with Direction Cosine Matrices.classQuaternion: For working with single quaternion representations.classQuaternionArray: For working with arrays of quaternions (useful for time-series data or multiple orientations).AHRS provides two main strategies for attitude estimation:
Most estimators are designed for low-cost strapdown navigation systems using:
To get the latest version from the git repository, clone the repository and install it manually using pip. This is the recommended method for accessing the most recent updates.
git clone https://github.com/Mayitzin/ahrs.git
cd ahrs/
python pip install .