构建首个面向复杂光照下小径导航的多模态数据集
TOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions
- 基于重复巡检采集多模态数据，涵盖128通道激光雷达与光照信息
- 动态多尺度融合模型在低光条件下实现92.3%的路径分割准确率
- 适合研究野外救援、森林火灾等场景下的自主导航算法
在非结构化户外环境中识别可通行路径对自主机器人仍具挑战性，尤其在广域搜救及森林火灾等应急场景中。现有数据集和模型多聚焦城市环境或宽幅车行道，缺乏对狭窄小径场景的支持。为此，我们提出面向此类环境的基于小径的非结构化多模态数据集（TOMD），包含128通道激光雷达、双目图像、GNSS、IMU及光照测量数据，通过多次不同光照条件下的巡检采集。同时提出一种动态多尺度数据融合模型，分析了早期、交叉与混合融合策略在不同光照下的表现。结果表明该方法有效，且光照显著影响分割性能。TOMD已公开发布于https://github.com/yyyxs1125/TMOD，以支持未来小径导航研究。
原文摘要 · Abstract (English)
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.
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