构建可扩展的视觉SLAM抗干扰评估框架，模拟雾、雨等恶劣条件下的系统表现。
SLAM Adversarial Lab: An Extensible Framework for Visual SLAM Robustness Evaluation under Adverse Conditions
- 将恶劣天气转化为可量化扰动，用真实单位（如米）控制强度
- 在三种数据集上测试七种SLAM算法，定位误差均在严重雾天超10米
- 支持自动搜索失效临界点，适合自动驾驶和机器人感知研究者使用
我们提出SLAM Adversarial Lab（SAL），一个模块化框架，用于在雾、雨等恶劣条件下评估视觉SLAM系统的鲁棒性。SAL将每种恶劣条件表示为对现有数据集的扰动，并通过易于理解的真实世界单位（如雾的能见度以米计）支持不同严重程度的设定。其可扩展架构通过统一接口解耦数据集、扰动和SLAM算法，用户可无需重写集成代码即可添加新组件。此外，SAL包含一种搜索过程，可自动找到导致SLAM系统失效的扰动严重程度。为展示能力，我们在三个数据集上整合了七种SLAM算法，评估其在天气、相机和视频传输扰动下的表现。
原文摘要 · Abstract (English)
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.
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