# Submodular Optimization for Keyframe Selection & Usage in SLAM

arXiv: https://arxiv.org/abs/2410.05576 · HTML: https://mlsanity.com/paper/2410.05576/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2024.

## Built on by (most cited first, 4 of 4)

- [Practical and Performant Enhancements for Maximization of Algebraic Connectivity](https://mlsanity.com/paper/2511.08694/) (2025)
- [Automatic Map Density Selection for Locally-Performant Visual Place Recognition](https://mlsanity.com/paper/2602.21473/) (2026)
- [Compact Keyframe-Optimized Multi-Agent Gaussian Splatting SLAM](https://mlsanity.com/paper/2604.00804/) (2026)
- [InLiER: Learning-Free Heterogeneous LiDAR Place Recognition via Intermediate Mixed-Radix Structural Keypoint Tokenization](https://mlsanity.com/paper/2607.16862/) (2026)

## Abstract (arXiv)

Keyframes are LiDAR scans saved for future reference in Simultaneous Localization And Mapping (SLAM), but despite their central importance most algorithms leave choices of which scans to save and how to use them to wasteful heuristics. This work proposes two novel keyframe selection strategies for localization and map summarization, as well as a novel approach to submap generation which selects keyframes that best constrain localization. Our results show that online keyframe selection and submap generation reduce the number of saved keyframes and improve per scan computation time without compromising localization performance. We also present a map summarization feature for quickly capturing environments under strict map size constraints.