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.
@article{arxiv.2503.03254,
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},
journal= {arXiv preprint arXiv:2503.03254},
year = {2025}
}
Comments
12 pages, 13 figurs, arxiv version for paper published at IROS 2025