SCORE: Saturated Consensus Relocalization in Semantic Line Maps

作者：Haodong Jiang, Xiang Zheng, Yanglin Zhang, Qingcheng Zeng, Y. G. Li, Ziyang Hong, Wu Junfeng · 年份：2025 · DOI：10.1109/iros60139.2025.11245940 · 被引用次数：1 · 研究领域：Robotics and Sensor-Based Localization、3D Shape Modeling and Analysis、Advanced Vision and Imaging

We present SCORE, a visual relocalization system that achieves unprecedented map compactness through 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 mechanism, Saturated Consensus Maximization (Sat-CM), which generalizes classical Consensus Maximization (CM) by assigning diminishing weights to inlier associations 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.