SCORE: Saturated Consensus Relocalization in Semantic Line Maps
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866915421910728704 |
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| author | Jiang, Haodong Zheng, Xiang Zhang, Yanglin Zeng, Qingcheng Li, Yiqian Hong, Ziyang Wu, Junfeng |
| author_facet | Jiang, Haodong Zheng, Xiang Zhang, Yanglin Zeng, Qingcheng Li, Yiqian Hong, Ziyang Wu, Junfeng |
| contents | We present SCORE, a visual relocalization system that achieves unprecedented map compactness by adopting semantically labeled 3D line maps. SCORE requires only 0.01\%-0.1\% of the storage needed by structure-based or learning-based baselines, while maintaining practical accuracy and comparable runtime. The key innovation is a novel robust estimation mechanism, Saturated Consensus Maximization (Sat-CM), which generalizes classical Consensus Maximization (CM) by assigning diminishing weights to inlier associations according to maximum likelihood with probabilistic justification. Under extreme outlier ratios (up to 99.5\%) arising from one-to-many ambiguity in semantic matching, Sat-CM enables accurate estimation when CM fails. To ensure computational efficiency, we propose an accelerating framework for globally solving Sat-CM formulations and specialize it for the Perspective-n-Lines problem at the core of SCORE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03254 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SCORE: Saturated Consensus Relocalization in Semantic Line Maps Jiang, Haodong Zheng, Xiang Zhang, Yanglin Zeng, Qingcheng Li, Yiqian Hong, Ziyang Wu, Junfeng Robotics We present SCORE, a visual relocalization system that achieves unprecedented map compactness by adopting semantically labeled 3D line maps. SCORE requires only 0.01\%-0.1\% of the storage needed by structure-based or learning-based baselines, while maintaining practical accuracy and comparable runtime. The key innovation is a novel robust estimation mechanism, Saturated Consensus Maximization (Sat-CM), which generalizes classical Consensus Maximization (CM) by assigning diminishing weights to inlier associations according to maximum likelihood with probabilistic justification. Under extreme outlier ratios (up to 99.5\%) arising from one-to-many ambiguity in semantic matching, Sat-CM enables accurate estimation when CM fails. To ensure computational efficiency, we propose an accelerating framework for globally solving Sat-CM formulations and specialize it for the Perspective-n-Lines problem at the core of SCORE. |
| title | SCORE: Saturated Consensus Relocalization in Semantic Line Maps |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.03254 |