SSR: A Generic Framework for Text-Aided Map Compression for Localization
Mapping is crucial in robotics for localization and downstream decision-making. As robots are deployed in ever-broader settings, the maps they rely on continue to increase in size. However, storing these maps indefinitely (cold storage), transferring them across networks, or sending localization queries to cloud-hosted maps imposes prohibitive memory and bandwidth costs. We propose a text-enhanced compression framework that reduces both memory and bandwidth footprints while retaining high-fidelity localization. The key idea is to treat text as an alternative modality: one that can be losslessly compressed with large language models. We propose leveraging lightweight text descriptions comb...
| Authors | Mohammad Omama, Po-han Li, Harsh Goel et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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