Factor Graphs in Optimization-Based Robotic Control—A Tutorial and Review

constrained factor graphs; Factor graphs; graph optimization; least squares optimization; model predictive control; robotic control; robotic perception; situational awareness; slam; Constrained factor graph; Graph optimization; Least square optimization; Model-predictive control; Robotic controls; Robotic perception; Simultaneous localization and mapping; Situational awareness; Computer Science (all); Materials Science (all); Engineering (all); Robots; Optimization; Probabilistic logic; Optimal control; Uncertainty; Tutorials; Reviews; Probability distribution; Heuristic algorithms

Abstract :

[en] Factor graphs, initially developed as probabilistic graphical models, have been widely employed for solving large-scale inference problems in robotics, particularly in tasks such as pose estimation, Structure from Motion (SfM), or Simultaneous Localization and Mapping (SLAM). Their capability to efficiently model uncertainty and the locality of sensor data has made them crucial for robotic perception and situational awareness. Recently, factor graphs have evolved beyond their probabilistic origins and are also being applied to deterministic optimization problems, such as robotic planning and control. This paper first aims to provide a comprehensive tutorial on factor graphs and the formulation and solution of the related optimization problems within the context of robotics perception. In addition, we undertake a thorough review of approaches that extend factor graphs—traditionally solved by unconstrained optimization—to optimal control tasks, emphasizing how they handle the constraints intrinsic to control problems. Finally, we analyze the potential of factor graphs for the seamless integration of robotic situational awareness, planning, and control, which remains one of the most critical challenges in achieving fully autonomous robot operations in complex environments.

Disciplines :

Electrical & electronics engineering

Author, co-author :

ABDELKARIM, Anas ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation ; RPTU University of Kaiserslautern-Landau, Department of Electrical and Computer Engineering, Kaiserslautern, Germany

VOOS, Holger ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation

Gorges, Daniel ; RPTU University of Kaiserslautern-Landau, Department of Electrical and Computer Engineering, Kaiserslautern, Germany

External co-authors :

yes

Language :

English

Title :

Factor Graphs in Optimization-Based Robotic Control—A Tutorial and Review

Publication date :

2025

Journal title :

IEEE Access

ISSN :

2169-3536

Publisher :

Institute of Electrical and Electronics Engineers Inc.

Volume :

Pages :

28315 - 28334

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

http://xplorestaging.ieee.org/ielx8/6287639/10820123/10855417.pdf?arnumber=10855417

FnR Project :

FNR17041397 - MOCCA - Multi-objective Adaptive Cruise Control Of Battery Electric Vehicles With Advanced Situational Awareness, 2022 (01/02/2023-31/01/2027) - Anas Abdelkarim

Funders :

Fonds National de la Recherche Luxembourg

Funding text :

This research was funded in whole, or in part, by the Luxembourg National Research Fund (FNR), MOCCA Project, ref. 17041397. For the purpose of open access, and in fulfilment of the obligations arising from the grant agreement, the author has applied a Creative Commons Attribution 4.0 International (CC BY 4.0) license to any Author Accepted Manuscript version arising from this submission.

Available on ORBilu :

since 15 April 2025

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