Keeping Less is More: Point Sparsification for Visual SLAM
This incremental improvement addresses efficiency issues for real-world applications like autonomous vehicles and drones, but does not introduce a new paradigm.
The paper tackles the memory and computational cost limitations in visual SLAM by proposing a point sparsification method that selects map points for bundle adjustment, achieving more accurate camera poses with about one-third of the map points and half the computation.
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.