  When adapting Simultaneous Mapping and Localization (SLAM) to real-world
applications, such as autonomous vehicles, drones, and augmented reality
devices, its memory footprint and computing cost are the two main factors
limiting the performance and the range of applications. In sparse feature based
SLAM algorithms, one efficient way for this problem is to limit the map point
size by selecting the points potentially useful for local and global bundle
adjustment (BA). This study proposes an efficient graph optimization for
sparsifying map points in SLAM systems. Specifically, we formulate a maximum
pose-visibility and maximum spatial diversity problem as a minimum-cost
maximum-flow graph optimization problem. The proposed method works as an
additional step in existing SLAM systems, so it can be used in both
conventional or learning based SLAM systems. By extensive experimental
evaluations we demonstrate the proposed method achieves even more accurate
camera poses with approximately 1/3 of the map points and 1/2 of the
computation.
