Visual Simultaneous Localization and Mapping (VSLAM) is a fundamental technology for robotics applications. While VSLAM research has achieved significant advancements, its robustness under challenging situations, such as poor lighting, dynamic environments, motion blur, and sensor failures, remains a challenging issue. To address these challenges, we introduce a novel RGB-D dataset designed for evaluating the robustness of VSLAM systems. The dataset comprises real-world indoor scenes with dynamic objects, motion blur, and varying illumination, as well as emulated camera failures, including lens dirt, condensation, underexposure, and overexposure. Additionally, we offer open-source scripts for injecting camera failures into any images, enabling further customization by the research community. Our experiments demonstrate that ORB-SLAM2, a traditional VSLAM algorithm, and TartanVO, a Deep Learning-based VO algorithm, can experience performance degradation under these challenging conditions. Therefore, this dataset and the camera failure open-source tools provide a valuable resource for developing more robust VSLAM systems capable of handling real-world challenges.

翻译：视觉同步定位与建图（VSLAM）是机器人应用中的一项基础技术。尽管VSLAM研究已取得显著进展，但其在恶劣条件下的鲁棒性——例如光照不足、动态环境、运动模糊及传感器故障——仍然是一个具有挑战性的问题。为应对这些挑战，我们引入了一个新颖的RGB-D数据集，专门用于评估VSLAM系统的鲁棒性。该数据集包含具有动态物体、运动模糊和变化光照的真实室内场景，以及模拟的相机故障，包括镜头污渍、冷凝、曝光不足和曝光过度。此外，我们提供了开源脚本，可将相机故障注入任意图像，供研究社区进一步定制使用。我们的实验表明，传统VSLAM算法ORB-SLAM2和基于深度学习的视觉里程计算法TartanVO，在这些挑战性条件下均可能出现性能下降。因此，该数据集及相机故障开源工具为开发能够应对真实世界挑战的、更鲁棒的VSLAM系统提供了宝贵资源。