提出LuSeg模型，高效分割月面正负障碍物，兼顾精度与速度。
LuSeg: Efficient Negative and Positive Obstacles Segmentation via Contrast-Driven Multi-Modal Feature Fusion on the Lunar
- 基于对比学习融合多模态特征，分阶段提升分割性能。
- 在月面数据集上实现94.8%的mIoU，推理速度达57Hz。
- 适合月球探测、自动驾驶等高实时性场景应用。
随着月球探测任务日益复杂，保障探测车自主安全的表面探索成为关键挑战。本文构建了月面模拟系统LESS和LunarSeg数据集，提供包含正负障碍物的RGB-D数据。提出新型两阶段分割网络LuSeg，通过对比学习强化第一阶段的RGB编码器与第二阶段深度编码器之间的语义一致性。在自建LunarSeg数据集及公开真实世界NPO道路障碍数据集上的实验表明，LuSeg在正负障碍物分割上达到领先性能，同时保持约57 Hz的高推理速度。相关代码与数据已开源。
原文摘要 · Abstract (English)
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.
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