@misc{79583, type = "Article", year = "2021", title = "OV2SLAM: A Fully Online and Versatile Visual SLAM for Real-Time Applications", journal = "Ieee Robotics And Automation Letters", editor = "Institute of Electrical and Electronics Engineers (IEEE)", volume = "6", number = "2", pages = "1399-1406", author = "Ferrera Maxime, Eudes Alexandre, Moras Julien, Sanfourche Martial, Le Besnerais Guy", url = "", organization = "", address = "FRANCE", doi = "https://doi.org/10.1109/LRA.2021.3058069", abstract = "
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 OV 2 SLAM, 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: https://github.com/ov2slam/ov2slam .
", key = "" }