Keeping Less is More: Point Sparsification for Visual SLAM. (arXiv:2207.00225v2 [cs.RO] UPDATED)

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 …

arxiv slam visual slam