Summary of Lusnar:a Lunar Segmentation, Navigation and Reconstruction Dataset Based on Muti-sensor For Autonomous Exploration, by Jiayi Liu et al.
LuSNAR:A Lunar Segmentation, Navigation and Reconstruction Dataset based on Muti-sensor for Autonomous Exploration
by Jiayi Liu, Qianyu Zhang, Xue Wan, Shengyang Zhang, Yaolin Tian, Haodong Han, Yutao Zhao, Baichuan Liu, Zeyuan Zhao, Xubo Luo
First submitted to arxiv on: 9 Jul 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 The proposed paper presents a new benchmark dataset for evaluating autonomous perception and navigation systems on lunar rovers. The dataset, called LuSNAR, provides high-precision ground truth labels for tasks such as semantic segmentation, 3D reconstruction, and autonomous navigation. The dataset consists of multi-task, multi-scene, and multi-label data including stereo image pairs, panoramic semantic labels, dense depth maps, LiDAR point clouds, and rover positions. To verify the usability of the dataset, the authors evaluated and analyzed algorithms for these tasks using the proposed benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The new lunar dataset, LuSNAR, is designed to help robots explore the moon on their own by recognizing different scenes and objects. The dataset has many types of data, like pictures, maps, and sensor readings, that can be used to test how well algorithms work for tasks like identifying what’s in a picture or building 3D models from images. To show how useful the dataset is, the authors tested some algorithms with it and got good results. |
Keywords
» Artificial intelligence » Multi task » Precision » Semantic segmentation
