ROD: RGB-Only Fast and Efficient Off-road Freespace Detection

• URL: http://arxiv.org/abs/2508.08697v1
• Date: Tue, 12 Aug 2025 07:41:20 GMT
• Title: ROD: RGB-Only Fast and Efficient Off-road Freespace Detection
• Authors: Tong Sun, Hongliang Ye, Jilin Mei, Liang Chen, Fangzhou Zhao, Leiqiang Zong, Yu Hu,
• Abstract summary: Off-road freespace detection is more challenging than on-road scenarios because of the blurred boundaries of traversable areas.<n>Previous state-of-the-art (SOTA) methods employ multi-modal fusion of RGB images and LiDAR data.<n>This paper presents a novel RGB-only approach for off-road freespace detection, named ROD, eliminating the reliance on LiDAR data.
• Score: 15.62982857392494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Off-road freespace detection is more challenging than on-road scenarios because of the blurred boundaries of traversable areas. Previous state-of-the-art (SOTA) methods employ multi-modal fusion of RGB images and LiDAR data. However, due to the significant increase in inference time when calculating surface normal maps from LiDAR data, multi-modal methods are not suitable for real-time applications, particularly in real-world scenarios where higher FPS is required compared to slow navigation. This paper presents a novel RGB-only approach for off-road freespace detection, named ROD, eliminating the reliance on LiDAR data and its computational demands. Specifically, we utilize a pre-trained Vision Transformer (ViT) to extract rich features from RGB images. Additionally, we design a lightweight yet efficient decoder, which together improve both precision and inference speed. ROD establishes a new SOTA on ORFD and RELLIS-3D datasets, as well as an inference speed of 50 FPS, significantly outperforming prior models.

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