Publication
WOLF: A modular estimation framework for robotics based on factor graphs
Journal Article (2022)
Journal
IEEE Robotics and Automation Letters
Pages
4710-4717
Volume
7
Number
2
Doc link
https://doi.org/10.1109/LRA.2022.3151404
File
Authors
Projects associated
LOGIMATIC: Tight integration of EGNSS and on-board sensors for port vehicle automation
MdM: Unit of Excellence María de Maeztu
EB-SLAM: Event-based simultaneous localization and mapping
TERRINET: The European robotics research infrastructure network
GAUSS: Galileo-EGNOS as an asset for UTM safety and security
EBCON: Motion estimation and control with event cameras
Abstract
This paper introduces WOLF, a C++ estimation framework based on factor graphs and targeted at mobile robotics. WOLF can be 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.
Categories
robots.
Author keywords
sensor fusion, SLAM
Scientific reference
J. Solà, J. Vallvé, J. Casals, J. Deray, M. Fourmy, D. Atchuthan, A. Corominas Murtra and J. Andrade-Cetto. WOLF: A modular estimation framework for robotics based on factor graphs. IEEE Robotics and Automation Letters, 7(2): 4710-4717, 2022.
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