Task-Oriented Semantic Compression for Localization at the Network Edge
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911494487146496 |
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| author | Fang, Zhengru Hu, Senkang Guo, Yu Deng, Yiqin Fang, Yuguang |
| author_facet | Fang, Zhengru Hu, Senkang Guo, Yu Deng, Yiqin Fang, Yuguang |
| contents | Achieving precise visual localization in GPS-limited urban environments poses significant challenges for resource-constrained mobile platforms, particularly under strict bandwidth, memory, and processing limitations. Inspired by mammalian spatial cognition, we propose a task-oriented communication framework in which bandwidth-limited endpoints equipped with multi-camera systems extract compact multi-view features and offload localization tasks to collaborative edge servers. We introduce the Orthogonally-constrained Variational Information Bottleneck encoder (O-VIB), which incorporates automatic relevance determination (ARD) to prune non-informative features while enforcing orthogonality to minimize redundancy. This enables efficient and accurate localization with minimal transmission overhead. Extensive evaluation on a real-world urban localization dataset demonstrates that O-VIB achieves high-precision localization under stringent bandwidth budgets, outperforming existing methods across diverse communication constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18317 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Task-Oriented Semantic Compression for Localization at the Network Edge Fang, Zhengru Hu, Senkang Guo, Yu Deng, Yiqin Fang, Yuguang Computer Vision and Pattern Recognition Networking and Internet Architecture Achieving precise visual localization in GPS-limited urban environments poses significant challenges for resource-constrained mobile platforms, particularly under strict bandwidth, memory, and processing limitations. Inspired by mammalian spatial cognition, we propose a task-oriented communication framework in which bandwidth-limited endpoints equipped with multi-camera systems extract compact multi-view features and offload localization tasks to collaborative edge servers. We introduce the Orthogonally-constrained Variational Information Bottleneck encoder (O-VIB), which incorporates automatic relevance determination (ARD) to prune non-informative features while enforcing orthogonality to minimize redundancy. This enables efficient and accurate localization with minimal transmission overhead. Extensive evaluation on a real-world urban localization dataset demonstrates that O-VIB achieves high-precision localization under stringent bandwidth budgets, outperforming existing methods across diverse communication constraints. |
| title | Task-Oriented Semantic Compression for Localization at the Network Edge |
| topic | Computer Vision and Pattern Recognition Networking and Internet Architecture |
| url | https://arxiv.org/abs/2504.18317 |