OV$^{2}$SLAM : A Fully Online and Versatile Visual SLAM for Real-Time Applications
This work provides a versatile, robust, and precise real-time visual SLAM solution for applications such as augmented reality, virtual reality, robotics, and autonomous driving, representing an incremental improvement in performance.
The paper presents OV²SLAM, a fully online visual SLAM algorithm that supports both monocular and stereo camera setups, various map scales, and frame rates from a few Hertz to several hundreds. It achieves state-of-the-art accuracy and real-time performance compared to competing algorithms.
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}.