SLAM Adversarial Lab: An Extensible Framework for Visual SLAM Robustness Evaluation under Adverse Conditions

Abstract:We present SAL (SLAM Adversarial Lab), a modular framework for evaluating visual SLAM systems under adversarial conditions such as fog and rain. SAL represents each adversarial condition as a perturbation that transforms an existing dataset into an adversarial dataset. When transforming a dataset, SAL supports severity levels using easily-interpretable real-world units such as meters for fog visibility. SAL's extensible architecture decouples datasets, perturbations, and SLAM algorithms through common interfaces, so users can add new components without rewriting integration code. Moreover, SAL includes a search procedure that finds the severity level of a perturbation at which a SLAM system fails. To showcase the capabilities of SAL, our evaluation integrates seven SLAM algorithms and evaluates them across three datasets under weather, camera, and video transport perturbations.

Accepted for publication at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Code: this https URL

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2603.17165 [cs.RO]

(or arXiv:2603.17165v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2603.17165 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.