论文标题
ORFD：用于越野自由空间检测的数据集和基准
ORFD: A Dataset and Benchmark for Off-Road Freespace Detection
论文作者
论文摘要
FreeSpace检测是自动驾驶技术的重要组成部分，并且在轨迹计划中起着重要作用。在过去的十年中，已证明基于深度学习的自由空间检测方法可行。但是，这些努力集中在城市道路环境上，由于缺乏越野基准，很少有针对越野自由空间检测的深度基于学习的方法。在本文中，我们介绍了ORFD数据集，据我们所知，该数据集是第一个越野自由空间检测数据集。 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.我们提出了一个名为Off-NET的新型网络，该网络将变压器架构统一以汇总本地和全球信息，以满足大型接收领域的自由空间检测任务的要求。我们还向动态融合激光雷达和RGB图像信息提出了交叉注意，以进行准确的越野自由空间检测。数据集和代码可公开可用athttps：//github.com/chaytonmin/off-net。
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.
