Many applications of Visual SLAM, such as augmented reality, virtual reality, robotics or autonomous driving, require versatile, robust and precise solutions, most often with real-time capability. In this work, we describe OV2SLAM, a fully online algorithm, handling both monocular and stereo camera setups, various map scales and frame-rates ranging from a few Hertz up to several hundreds. It combines numerous recent contributions in visual localization within an efficient multi-threaded architecture. Extensive comparisons with competing algorithms shows the state-of-the-art accuracy and real-time performance of the resulting algorithm. For the benefit of the community, we release the source code: \url{https://github.com/ov2slam/ov2slam}.
@article{arxiv.2102.04060,
title = {OV$^{2}$SLAM : A Fully Online and Versatile Visual SLAM for Real-Time Applications},
author = {Maxime Ferrera and Alexandre Eudes and Julien Moras and Martial Sanfourche and Guy Le Besnerais},
journal= {arXiv preprint arXiv:2102.04060},
year = {2021}
}
Comments
Accepted for publication in IEEE Robotics and Automation Letters (RA-L). Code is available at : \url{https://github.com/ov2slam/ov2slam}