  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:
\url{https://github.com/ov2slam/ov2slam}.
