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

Detecting traversable pathways in unstructured out-door environments remains a significant challenge for autonomous robots, especially in critical applications such as wide-area search and rescue, as well as in incident management scenarios such as forest fires. Current datasets and models primarily focus on either urban environments or wide vehicle-traversable off-road tracks, leaving a substantial gap in tackling the complexities of trail-based off-road scenarios. To address this issue, we introduce the Trail-based Off-road Multimodal Dataset (TOMD), a comprehensive dataset explicitly designed for narrow and unstructured trail-like environments. Our dataset features high-fidelity multimodal sensor data — including 128-channel LiDAR, stereo imagery, GNSS, IMU, and illumination measurements — collected through repeated runs across di-verse environmental conditions. In addition, we propose a novel dynamic multiscale data fusion model for precise traversable pathway prediction in trail-like areas. The study investigates the impact of various fusion processes — early, cross, and mixed — on model performance under different illumination levels: low-light, normal ambient lighting, and bright conditions. The results highlight the effectiveness of our approach, variation in performance across illumination levels, and the potential applicability of the dataset in diverse environmental conditions.Our work provides a valuable resource for advancing trail-based off-road navigation, and we openly publish our TOMD at https://github.com/yyyxs1125/TMOD to establish a future bench-mark in this research domain.