ORFD: A Dataset and Benchmark for Off-Road Freespace Detection (2206.09907v2)

Abstract: 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.

• The paper introduces ORFD, a novel dataset and benchmark specifically for off-road freespace detection aimed at enhancing autonomous navigation in challenging terrains.
• It details a robust methodology for data collection and evaluation using diverse off-road scenarios to validate practical performance.
• The benchmark establishes clear evaluation metrics that enable researchers to compare and improve detection algorithms systematically.

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