RIANN is a lightweight neural network implementation for estimating orientation (attitude) from inertial measurement unit (IMU) data. It processes accelerometer and gyroscope readings to provide quaternion-based attitude estimation, optimized for real-time applications.

• Fast and accurate quaternion-based attitude estimation
• Optimized for real-time processing with state preservation
• Supports both batch processing and step-by-step integration
• Robust against sensor noise and motion artifacts
• Simple Python API with minimal dependencies

pip install riann

or from source

git clone https://github.com/daniel-om-weber/riann.git
cd riann
pip install -e .

import numpy as np
from riann.riann import RIANN

# Initialize RIANN
riann = RIANN()

# Prepare IMU data
acc = np.ones((100, 3))  # Accelerometer data (100 samples, XYZ axes)
gyr = np.zeros((100, 3)) # Gyroscope data (100 samples, XYZ axes)
fs = 200                 # Sampling rate in Hz

# Get attitude quaternions (w,x,y,z)
attitude = riann.predict(acc, gyr, fs)
print(f"Output shape: {attitude.shape}")  # (100, 4) - 100 unit quaternions

Output shape: (100, 4)