Paper Info
Title
WOLF: A Modular Estimation Framework for Robotics Based on Factor Graphs
Abstract
This letter introduces WOLF, a C++ estimation framework based on factor graphs and targeted at mobile robotics. WOLF can he used beyond SLAM to handle self-calibration, model identification, or the observation of dynamic quantities other than localization. The architecture of WOLF allows for a modular yet tightly-coupled estimator. Modularity is enhanced via reusable plugins that are loaded at runtime depending on application setup. This setup is achieved conveniently through YAML files, allowing users to configure a wide range of applications without the need of writing or compiling code. Most procedures are coded as abstract algorithms in base classes with varying levels of specialization. Overall, all these assets allow for coherent processing and favor code re-usability and scalability. WOLF can be used with ROS, and is made publicly available and open to collaboration.
Year
DOI
Venue
2022
10.1109/LRA.2022.3151404
IEEE ROBOTICS AND AUTOMATION LETTERS
Keywords
DocType
Volume
Sensor Fusion, SLAM
Journal
7
Issue
ISSN
Citations 
2
2377-3766
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Joan Solà127719.66
Joan Vallve-Navarro200.34
Joaquim Casals300.34
Jeremie Deray422.07
Mederic Fourmy501.01
Dinesh Atchuthan611.04
Juan Andrade-Cetto71404251.42