There are 4 repositories under factor-graph topic.
A general and flexible factor graph non-linear least square optimization framework
A graph-based multi-sensor fusion framework. It can be used to fuse various relative or absolute measurments with IMU readings in real-time.
Factor graphs and loopy belief propagation implemented in Python
Julia package for automatically generating Bayesian inference algorithms through message passing on Forney-style factor graphs.
Factored inference for discrete-continuous smoothing and mapping.
Overview and implementation of Belief Propagation and Loopy Belief Propagation algorithms: sum-product, max-product, max-sum
Lightweighted graph optimization (Factor graph) library.
Robust GNSS Processing With Factor Graphs
Official repo for the paper "SAGE: SLAM with Appearance and Geometry Prior for Endoscopy" (ICRA 2022)
State Estimation for SLAM: Filter(EKF, Particle Filter), MAP(GN, LM), Solver(Ceres-Solver, G2O, GTSAM), Bundle Adjustment
Software Release for "Incremental Covariance Estimation for Robust Localization"
Code release for "Evaluation of Precise Point Positioning Convergence with an Incremental Graph Optimizer".
Belief propagation with sparse matrices (scipy.sparse) in Python for LDPC codes. Includes NumPy implementation of message passing (min-sum and sum-product) and a few other decoders.
The algorithm solves the DC state estimation problem in electric power systems using the Gaussian belief propagation over factor graphs.
General purpose C++ library for managing discrete factor graphs
The FactorGraph package provides the set of different functions to perform inference over the factor graph with continuous or discrete random variables using the belief propagation algorithm.
Software release for "Enabling Robust State Estimation through Measurement Error Covariance Adaptation"
Factor graph visualization with d3.js
Gaussian belief propagation solver for noisy linear systems with real coefficients and variables.
Offline Simultaneous Localization and Mapping using GTSAM
Implementation of the Belief Propagation Side Channel Attack
A Probabilistic Graphical approach to detect different types of shilling attacks on Recommender Systems.
Experimental platform to achieve fused environment perception using different modalities of sensors
Factor potentials for factor graphs, Bayesian networks, and Markov random fields
Developed and implemented 2D and 3D Pose Graph SLAM using the GTSAM library and Gauss Newton Solver on the Intel and Parking Garage g2o datasets respectively
深蓝学院多传感器融合定位 Implementation of Multi-sensor Fusion SLAM Algorithms
Pratice and example to solve the pose graph SLAM problem using GTSAM
A short python script to visualize factor graphs passed in as matrix inputs.
Pure python implementation of minisam.
Learn a Factor Graph, or Markov Random Field (MRF), from data/observations. I.e. do PGM parameter learning.