提出首个可量化感知混淆的拓扑地图评测框架
TOPO-Bench: An Open-Source Topological Mapping Evaluation Framework with Quantifiable Perceptual Aliasing
- 以定位精度为拓扑一致性代理指标，实现可衡量评估
- 构建含校准模糊度的多样化基准数据集，支持公平对比
- 开源全部数据、基线与工具，推动领域可复现研究
拓扑地图提供紧凑且鲁棒的导航表征，但该领域进展受限于缺乏标准化的评估指标、数据集和协议。现有系统在不同环境和标准下评估，难以进行公平、可复现的比较。尤其关键的感知混淆问题长期缺乏量化，却显著影响系统性能。本文通过（1）将拓扑一致性形式化为基本属性，并证明定位精度可作为高效且可解释的代理指标；（2）提出首个数据集模糊性的量化度量方法，实现跨环境公平比较。为此，我们构建了一个包含校准模糊度水平的多样化基准数据集，实现并开源深度学习基线系统，与经典方法一同评估。实验分析揭示了当前方法在感知混淆下的局限性。所有数据集、基线及评估工具均完全开源，以促进拓扑地图研究的统一与可复现。
原文摘要 · Abstract (English)
Topological mapping offers a compact and robust representation for navigation, but progress in the field is hindered by the lack of standardized evaluation metrics, datasets, and protocols. Existing systems are assessed using different environments and criteria, preventing fair and reproducible comparisons. Moreover, a key challenge - perceptual aliasing - remains under-quantified, despite its strong influence on system performance. We address these gaps by (1) formalizing topological consistency as the fundamental property of topological maps and showing that localization accuracy provides an efficient and interpretable surrogate metric, and (2) proposing the first quantitative measure of dataset ambiguity to enable fair comparisons across environments. To support this protocol, we curate a diverse benchmark dataset with calibrated ambiguity levels, implement and release deep-learned baseline systems, and evaluate them alongside classical methods. Our experiments and analysis yield new insights into the limitations of current approaches under perceptual aliasing. All datasets, baselines, and evaluation tools are fully open-sourced to foster consistent and reproducible research in topological mapping.
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