SCORE: Saturated Consensus Relocalization in Semantic Line Maps
Haodong Jiang, Xiang Zheng, Yanglin Zhang, Qingcheng Zeng, Yiqian Li, Ziyang Hong, Junfeng Wu
Abstract
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.
BibTeX
@inproceedings{iros2025_scoresaturatedco,
title = {SCORE: Saturated Consensus Relocalization in Semantic Line Maps},
author = {Haodong Jiang and Xiang Zheng and Yanglin Zhang and Qingcheng Zeng and Yiqian Li and Ziyang Hong and Junfeng Wu},
booktitle = {IROS 2025},
year = {2025}
}