用优化方法选关键帧，省空间还提速。
Submodular Optimization for Keyframe Selection & Usage in SLAM
- 基于子模函数设计关键帧选择策略
- 关键帧减少40%以上，计算更快且定位不丢精度
- 适合资源受限的实时定位系统
在同时定位与地图构建（SLAM）中，关键帧是保存的激光雷达扫描数据，对后续定位和建图至关重要。然而现有算法多依赖低效启发式规则来决定保存哪些扫描数据以及如何使用。本文提出两种新型关键帧选择策略，分别用于定位优化和地图压缩，并设计了一种新子图生成方法，通过选择最能约束定位的关键帧来提升效率。实验表明，在线关键帧选择与子图生成可显著减少保存的关键帧数量，同时提升每帧处理速度，且不影响定位性能。此外，还引入一种地图压缩功能，可在严格地图大小限制下快速捕获环境信息。
原文摘要 · Abstract (English)
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.
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