SSR: A Generic Framework for Text-Aided Map Compression for Localization
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912942733131776 |
|---|---|
| author | Omama, Mohammad Li, Po-han Goel, Harsh Choi, Minkyu Chalaki, Behdad Tadiparthi, Vaishnav Mahjoub, Hossein Nourkhiz Pari, Ehsan Moradi Chinchali, Sandeep P. |
| author_facet | Omama, Mohammad Li, Po-han Goel, Harsh Choi, Minkyu Chalaki, Behdad Tadiparthi, Vaishnav Mahjoub, Hossein Nourkhiz Pari, Ehsan Moradi Chinchali, Sandeep P. |
| contents | 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 combined with very small image feature vectors, which capture "complementary information" as a compact representation for the mapping task. Building on this, our novel technique, Similarity Space Replication (SSR), learns an adaptive image embedding in one shot that captures only the information "complementary" to the text descriptions. We validate our compression framework on multiple downstream localization tasks, including Visual Place Recognition as well as object-centric Monte Carlo localization in both indoor and outdoor settings. SSR achieves 2 times better compression than competing baselines on state-of-the-art datasets, including TokyoVal, Pittsburgh30k, Replica, and KITTI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_04272 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SSR: A Generic Framework for Text-Aided Map Compression for Localization Omama, Mohammad Li, Po-han Goel, Harsh Choi, Minkyu Chalaki, Behdad Tadiparthi, Vaishnav Mahjoub, Hossein Nourkhiz Pari, Ehsan Moradi Chinchali, Sandeep P. Computer Vision and Pattern Recognition 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 combined with very small image feature vectors, which capture "complementary information" as a compact representation for the mapping task. Building on this, our novel technique, Similarity Space Replication (SSR), learns an adaptive image embedding in one shot that captures only the information "complementary" to the text descriptions. We validate our compression framework on multiple downstream localization tasks, including Visual Place Recognition as well as object-centric Monte Carlo localization in both indoor and outdoor settings. SSR achieves 2 times better compression than competing baselines on state-of-the-art datasets, including TokyoVal, Pittsburgh30k, Replica, and KITTI. |
| title | SSR: A Generic Framework for Text-Aided Map Compression for Localization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.04272 |