LuSeg：通过对比驱动的多模态特征融合实现高效的正负障碍物分割（面向月球）
LuSeg: Efficient Negative and Positive Obstacles Segmentation via Contrast-Driven Multi-Modal Feature Fusion on the Lunar
AI总结：
该研究构建 LESS 仿真系统与 LunarSeg RGB-D 数据集，并提出两阶段对比学习网络 LuSeg，实现月球正负障碍物的高精度、约57 Hz实时分割。
摘要：
随着月球探测任务日益复杂，确保巡视器安全、自主地开展月面巡视已成为月球探测任务中的关键挑战之一。在本研究中，我们开发了名为 Lunar Exploration Simulator System（LESS）的月面仿真系统以及 LunarSeg 数据集，为同时包含正、负障碍物的月球障碍物分割提供 RGB-D 数据。此外，我们提出了一种名为 LuSeg 的新型两阶段分割网络。该网络通过对比学习，强制阶段 I 的 RGB 编码器与阶段 II 的深度编码器之间保持语义一致性。在我们提出的 LunarSeg 数据集以及额外的公开真实世界 NPO 道路障碍物数据集上的实验结果表明，LuSeg 对正障碍物和负障碍物均取得了最先进的分割性能，同时保持约 57 Hz 的高推理速度。我们已在 https://github.com/nubot-nudt/LuSeg 发布 LESS 系统的实现、LunarSeg 数据集以及 LuSeg 的代码。
英文摘要：
As lunar exploration missions grow increasingly complex, ensuring safe and autonomous rover-based surface exploration has become one of the key challenges in lunar exploration tasks. In this work, we have developed a lunar surface simulation system called the Lunar Exploration Simulator System (LESS) and the LunarSeg dataset, which provides RGB-D data for lunar obstacle segmentation that includes both positive and negative obstacles. Additionally, we propose a novel two-stage segmentation network called LuSeg. Through contrastive learning, it enforces semantic consistency between the RGB encoder from Stage I and the depth encoder from Stage II. Experimental results on our proposed LunarSeg dataset and additional public real-world NPO road obstacle dataset demonstrate that LuSeg achieves state-of-the-art segmentation performance for both positive and negative obstacles while maintaining a high inference speed of approximately 57\,Hz. We have released the implementation of our LESS system, LunarSeg dataset, and the code of LuSeg at:https://github.com/nubot-nudt/LuSeg.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
