  Freespace detection is an essential component of autonomous driving
technology and plays an important role in trajectory planning. In the last
decade, deep learning-based free space detection methods have been proved
feasible. However, these efforts were focused on urban road environments and
few deep learning-based methods were specifically designed for off-road free
space detection due to the lack of off-road benchmarks. In this paper, we
present the ORFD dataset, which, to our knowledge, is the first off-road free
space detection dataset. The dataset was collected in different scenes
(woodland, farmland, grassland, and countryside), different weather conditions
(sunny, rainy, foggy, and snowy), and different light conditions (bright light,
daylight, twilight, darkness), which totally contains 12,198 LiDAR point cloud
and RGB image pairs with the traversable area, non-traversable area and
unreachable area annotated in detail. We propose a novel network named OFF-Net,
which unifies Transformer architecture to aggregate local and global
information, to meet the requirement of large receptive fields for free space
detection tasks. We also propose the cross-attention to dynamically fuse LiDAR
and RGB image information for accurate off-road free space detection. Dataset
and code are publicly available athttps://github.com/chaytonmin/OFF-Net.
