TOPO-Bench: An Open-Source Topological Mapping Evaluation Framework with Quantifiable Perceptual Aliasing

2025-10-05

AI Summary

Topological mapping has long suffered from the absence of standardized evaluation metrics, benchmark datasets, and reproducible protocols—particularly lacking quantitative characterization of perceptual aliasing—hindering fair method comparison and reliability improvement. To address this, we introduce the first open-source topological mapping evaluation framework. First, we formalize topological consistency, using localization accuracy as a proxy metric. Second, we propose the first quantifiable ambiguity metric and release a benchmark dataset covering diverse scenarios with calibrated aliasing levels. Third, we develop an integrated evaluation toolchain incorporating both classical and deep-learning-based baseline systems. Experimental results expose critical performance bottlenecks of existing methods under perceptual aliasing, thereby advancing reproducible research and robust navigation.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Standardizing evaluation metrics for topological mapping systems
• Quantifying perceptual aliasing impact on mapping performance
• Creating benchmark datasets with calibrated ambiguity levels

Innovation

• Formalizing topological consistency as fundamental map property
• Proposing quantitative dataset ambiguity measure for comparisons
• Releasing open-source benchmark with calibrated ambiguity levels