用语义线图实现极紧凑定位，存储仅需传统方法千分之一。
SCORE: Saturated Consensus Relocalization in Semantic Line Maps
- 基于语义3D线图构建，用新算法处理高噪声匹配
- 在99.5%异常值下仍保持准确，存储仅需0.01%-0.1%
- 适合资源受限场景的实时高精度定位应用
我们提出SCORE，一种视觉重定位系统，通过采用语义标注的3D线图实现前所未有的地图紧凑性。与基于结构或学习的基线方法相比，SCORE仅需0.01%–0.1%的存储空间，同时保持实用精度和相当的运行时。核心创新是新型鲁棒估计机制——饱和共识最大化（Sat-CM），它通过最大似然与概率论证为内点关联分配递减权重，对经典共识最大化（CM）进行推广。在语义匹配中因一对多歧义导致高达99.5%异常值的情况下，该方法仍能实现准确估计，而传统CM已失效。为保证计算效率，我们提出一种全局求解Sat-CM的加速框架，并专门优化用于核心问题Perspective-n-Lines。
原文摘要 · Abstract (English)
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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