WOLF: A modular estimation framework for robotics based on factor graphs

TL;DR

WOLF is a flexible, modular C++ estimation framework for robotics based on factor graphs, supporting diverse applications like SLAM, self-calibration, and dynamic observation, with easy configuration and open-source availability.

Contribution

The paper presents WOLF, a novel modular estimation framework that enhances reusability, scalability, and ease of configuration for various robotics estimation tasks.

Findings

Supports multiple robotics estimation tasks beyond SLAM

Enables easy configuration via YAML files

Open-source and ROS-compatible implementation

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.