Papers › SCORE: Saturated Consensus Relocalization in Semantic Line Maps
SCORE: Saturated Consensus Relocalization in Semantic Line Maps
Haodong Jiang, Xiang Zheng, Yanglin Zhang, Qingcheng Zeng, Yiqian Li, Ziyang Hong, Junfeng Wu
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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.
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