TOPO-Bench: An Open-Source Topological Mapping Evaluation Framework with Quantifiable Perceptual Aliasing
arXiv:2510.04100v2 Announce Type: replace-cross 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 topologic
The proliferation of autonomous systems reliant on robust navigation necessitates standardized evaluation for topological mapping, which this framework addresses by quantifying perceptual aliasing.
Standardized evaluation frameworks for topological mapping are critical for accelerating the development and deployment of reliable AI agents and robotic systems.
The ability to formally compare and benchmark different topological mapping approaches with quantifiable metrics, including perceptual aliasing, will lead to more consistent and robust system development.
- · AI/Robotics researchers
- · Autonomous system developers
- · Robotics companies
- · Developers relying on ad-hoc benchmarking
- · Systems with poor perceptual aliasing handling
Improved topological mapping algorithms emerge due to standardized, quantifiable evaluations.
More reliable autonomous robots and AI agents are developed and deployed in diverse environments.
The enhanced capability of autonomous systems accelerates their integration across various industries, from logistics to exploration.
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Read at arXiv cs.AI