Factorama

A factor graph library with emphasis on simple usage. Suitable for small-medium SLAM, calibration, and SFM problems.

Available for both Python and C++.

Features

• Factor Graph Framework: Flexible factor graph implementation for bundle adjustment
• Variable Types: Support for 6-DOF poses, 3D landmarks, inverse depth parameterization, and extrinsic calibration
• Sparse Matrix Support: Uses Eigen sparse matrices to speed up computation
• Solvers: Levenberg-Marquardt and Gauss-Newton algorithms
• Multiple Factor Types: Bearing observations, pose priors, relative constraints, and camera-IMU alignment

Quick Start

Python

Install via pip:

pip install factorama

Basic usage:

import factorama
from factorama import FactorGraph, PoseVariable, LandmarkVariable

# Create factor graph
graph = FactorGraph()

# Add variables and factors
# ... (see Python guide for details)

# Optimize
graph.finalize_structure()
optimizer = factorama.SparseOptimizer()
optimizer.setup(graph, settings)
optimizer.optimize()

→ Full Python Documentation

C++

Build from source:

git clone https://github.com/steven-gilbert-az/factorama.git
cd factorama
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j$(nproc)
sudo make install

→ Full C++ Documentation

Contributing

Please see CONTRIBUTING.md for development guidelines, coding standards, and contribution workflow.

License

This project is licensed under the MIT License - see the LICENSE.txt file for details.

Citation

If you use this library in academic work, please cite:

[Citation information to be added]