标题： TOMD：一种基于路径的非公路多模态数据集，用于在具有挑战性的光照条件下可行驶路径分割 显示英文标题

标题： TOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions

摘要： 在非结构化户外环境中检测可通行路径对于自主机器人仍然是一个重大挑战，尤其是在广域搜索和救援等关键应用，以及森林火灾等事件管理场景中。 现有的数据集和模型主要针对城市环境或宽广的可行驶非公路路线，而在狭窄、类似小径的非公路场景方面存在显著的空白。 为了解决这一问题，我们引入了基于小径的非公路多模态数据集（TOMD），这是一个专门为这类环境设计的全面数据集。 TOMD包含高保真多模态传感器数据——包括128通道激光雷达、立体图像、GNSS、IMU和光照测量值——在不同条件下通过重复穿越收集。 我们还提出了一种动态多尺度数据融合模型，用于准确的可通行路径预测。 该研究分析了在不同光照水平下早期融合、交叉融合和混合融合策略的性能。 结果表明了我们方法的有效性以及光照在分割性能中的相关性。 我们已在https://github.com/yyyxs1125/TMOD公开发布TOMD，以支持基于小径的非公路导航的未来研究。 显示英文摘要

摘要： Detecting traversable pathways in unstructured outdoor environments remains a significant challenge for autonomous robots, especially in critical applications such as wide-area search and rescue, as well as incident management scenarios like forest fires. Existing datasets and models primarily target urban settings or wide, vehicle-traversable off-road tracks, leaving a substantial gap in addressing the complexity of narrow, trail-like off-road scenarios. To address this, we introduce the Trail-based Off-road Multimodal Dataset (TOMD), a comprehensive dataset specifically designed for such environments. TOMD features high-fidelity multimodal sensor data -- including 128-channel LiDAR, stereo imagery, GNSS, IMU, and illumination measurements -- collected through repeated traversals under diverse conditions. We also propose a dynamic multiscale data fusion model for accurate traversable pathway prediction. The study analyzes the performance of early, cross, and mixed fusion strategies under varying illumination levels. Results demonstrate the effectiveness of our approach and the relevance of illumination in segmentation performance. We publicly release TOMD at https://github.com/yyyxs1125/TMOD to support future research in trail-based off-road navigation.