QueensCAMP: an RGB-D dataset for robust Visual SLAM

• URL: http://arxiv.org/abs/2410.12520v1
• Date: Wed, 16 Oct 2024 12:58:08 GMT
• Title: QueensCAMP: an RGB-D dataset for robust Visual SLAM
• Authors: Hudson M. S. Bruno, Esther L. Colombini, Sidney N. Givigi Jr,
• Abstract summary: 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. We offer open-source scripts for injecting camera failures into any images, enabling further customization.
• Score: 0.0
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• Abstract: 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.

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