  Monocular visual SLAM has become an attractive practical approach for robot
localization and 3D environment mapping, since cameras are small, lightweight,
inexpensive, and produce high-rate, high-resolution data streams. Although
numerous robust tools have been developed, most existing systems are designed
to operate in terrestrial environments and at relatively small scale (a few
thousand frames) due to constraints on computation and storage.
  In this paper, we present a feature-based visual SLAM system for aerial video
whose simple design permits near real-time operation, and whose scalability
permits large-area mapping using tens of thousands of frames, all on a single
conventional computer. Our approach consists of two parallel threads: the first
incrementally creates small locally consistent submaps and estimates camera
poses at video rate; the second aligns these submaps with one another to
produce a single globally consistent map via factor graph optimization over
both poses and landmarks. Scale drift is minimized through the use of
7-degree-of-freedom similarity transformations during submap alignment.
  We quantify our system's performance on both simulated and real data sets,
and demonstrate city-scale map reconstruction accurate to within 2 meters using
nearly 90,000 aerial video frames - to our knowledge, the largest and fastest
such reconstruction to date.
