  This paper presents ORB-SLAM, a feature-based monocular SLAM system that
operates in real time, in small and large, indoor and outdoor environments. The
system is robust to severe motion clutter, allows wide baseline loop closing
and relocalization, and includes full automatic initialization. Building on
excellent algorithms of recent years, we designed from scratch a novel system
that uses the same features for all SLAM tasks: tracking, mapping,
relocalization, and loop closing. A survival of the fittest strategy that
selects the points and keyframes of the reconstruction leads to excellent
robustness and generates a compact and trackable map that only grows if the
scene content changes, allowing lifelong operation. We present an exhaustive
evaluation in 27 sequences from the most popular datasets. ORB-SLAM achieves
unprecedented performance with respect to other state-of-the-art monocular SLAM
approaches. For the benefit of the community, we make the source code public.
