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Many applications of Visual SLAM, such as augmented reality, virtual reality,robotics or autonomous driving, require versatile, robust and precisesolutions, most often with real-time capability. In this work, we describeOV$^{2}$SLAM, a fully online algorithm, handling both monocular and stereocamera setups, various map scales and frame-rates ranging from a few Hertz upto several hundreds. It combines numerous recent contributions in visuallocalization within an efficient multi-threaded architecture. Extensivecomparisons with competing algorithms shows the state-of-the-art accuracy andreal-time performance of the resulting algorithm. For the benefit of thecommunity, we release the source code:\url{https://github.com/ov2slam/ov2slam}.

OV$^{2}$SLAM : A Fully Online and Versatile Visual SLAM for Real-Time Applications