In this paper, we emphasise the critical importance of large-scale datasets for advancing field robotics capabilities, particularly in natural environments. While numerous datasets exist for urban and suburban settings, those tailored to natural environments are scarce. Our recent benchmarks WildPlaces and WildScenes address this gap by providing synchronised image, lidar, semantic and accurate 6-DoF pose information in forest-type environments. We highlight the multi-modal nature of this dataset and discuss and demonstrate its utility in various downstream tasks, such as place recognition and 2D and 3D semantic segmentation tasks.

翻译：本文强调了大规模数据集对于提升野外机器人技术能力的关键作用，尤其是在自然环境中。尽管当前存在大量面向城市和郊区的数据集，但针对自然环境的专用数据集仍然稀缺。我们近期提出的WildPlaces与WildScenes基准数据集填补了这一空白，通过提供森林型环境中同步采集的图像、激光雷达、语义标签及精确六自由度位姿信息。本文重点阐述了该数据集的多模态特性，并通过位姿识别、二维及三维语义分割等下游任务实例，论证了其实际应用价值。