Summary of Queenscamp: An Rgb-d Dataset For Robust Visual Slam, by Hudson M. S. Bruno et al.
QueensCAMP: an RGB-D dataset for robust Visual SLAM
by Hudson M. S. Bruno, Esther L. Colombini, Sidney N. Givigi Jr
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel RGB-D dataset is introduced to evaluate the robustness of Visual Simultaneous Localization and Mapping (VSLAM) systems under challenging conditions, including poor lighting, dynamic environments, motion blur, and sensor failures. The dataset features real-world indoor scenes with varying illumination, emulated camera failures, and open-source scripts for injecting camera failures into images. Experiments show that traditional VSLAM algorithms like ORB-SLAM2 and Deep Learning-based VO algorithms like TartanVO can experience performance degradation under these conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Visual Simultaneous Localization and Mapping (VSLAM) is a technology used in robotics to help robots navigate and map their surroundings. But it’s not very good at handling things that make life difficult, like bad lighting or moving objects. To fix this, scientists created a special set of pictures and videos called a dataset. This dataset has lots of different scenes with varying light levels and some fake problems that could happen to cameras, like dirt or water on the lens. They also gave away the code to add these problems to any images, so other researchers can use it too. The scientists tested some popular VSLAM algorithms and found that they all got worse when things got tough. |
Keywords
» Artificial intelligence » Deep learning
