We present ConFusion, an open-source package for online sensor fusion for robotic applications. ConFusion is a modular framework for fusing measurements from many heterogeneous sensors within a moving horizon estimator. ConFusion offers greater flexibility in sensor fusion problem design than filtering-based systems and the ability to scale the online estimate quality with the available computing power. We demonstrate its performance in comparison to an iterated extended Kalman filter in visual-inertial tracking, and show its versatility through whole-body sensor fusion on a mobile manipulator.
@article{arxiv.1806.07115,
title = {ConFusion: Sensor Fusion for Complex Robotic Systems using Nonlinear Optimization},
author = {Timothy Sandy and Lukas Stadelmann and Simon Kerscher and Jonas Buchli},
journal= {arXiv preprint arXiv:1806.07115},
year = {2019}
}