仅用RGB图像实现高速高精度非铺装路面空域检测
ROD: RGB-Only Fast and Efficient Off-road Freespace Detection
- 基于预训练ViT提取RGB图像特征，结合轻量解码器提升效率
- 在ORFD和RELLIS-3D上达到新SOTA，推理速度达50 FPS
- 适合对实时性要求高的户外机器人导航场景
非铺装路面空域检测比铺装路面更具挑战性，因可通行区域边界模糊。以往先进方法依赖RGB图像与激光雷达（LiDAR）的多模态融合，但计算表面法向图导致推理延迟显著增加，难以满足实时应用需求，尤其在需要更高帧率的野外场景中。本文提出一种全新的仅使用RGB图像的非铺装路面空域检测方法ROD，摆脱对激光雷达及其计算开销的依赖。具体而言，采用预训练视觉变换器（ViT）从RGB图像中提取丰富特征，并设计一个轻量高效的解码器，在保证精度的同时大幅提升推理速度。ROD在ORFD和RELLIS-3D数据集上均达到新最优性能，推理速度达50 FPS，显著优于先前模型。
原文摘要 · Abstract (English)
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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