QueensCAMP: an RGB-D dataset for robust Visual SLAM
This provides a resource for developing more robust VSLAM systems, but it is incremental as it focuses on dataset creation rather than a new method.
The authors tackled the problem of robustness in Visual SLAM by introducing a new RGB-D dataset with challenging conditions like dynamic objects, motion blur, and camera failures, showing that existing algorithms like ORB-SLAM2 and TartanVO experience performance degradation.
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.