# Keeping Less is More: Point Sparsification for Visual SLAM

arXiv: https://arxiv.org/abs/2207.00225 · HTML: https://mlsanity.com/paper/2207.00225/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 41 references that are in the map.
- First posted on arXiv 1 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot](https://mlsanity.com/paper/2007.01813/) (2020)
- [Stochastic Bundle Adjustment for Efficient and Scalable 3D Reconstruction](https://mlsanity.com/paper/2008.00446/) (2020)
- [From Monocular SLAM to Autonomous Drone Exploration](https://mlsanity.com/paper/1609.07835/) (2016)
- [Monocular LSD-SLAM Integration within AR System](https://mlsanity.com/paper/1702.02514/) (2017)
- [Complexity Analysis and Efficient Measurement Selection Primitives for High-Rate Graph SLAM](https://mlsanity.com/paper/1709.06821/) (2017)

## Built on by (most cited first, 1 of 1)

- [P2U-SLAM: A Monocular Wide-FoV SLAM System Based on Point Uncertainty and Pose Uncertainty](https://mlsanity.com/paper/2409.10143/) (2024)

## Abstract (arXiv)

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.