Abstract
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.
Abstract (translated)
在本文中，我们强调了大规模数据集在推动机器人技术进步，特别是在自然环境中的重要性。虽然有许多数据集适用于城市和郊区环境，但专门针对自然环境的却很少。我们最近的大规模基准WildPlaces和WildScenes通过提供同步的图像、激光雷达、语义和精确的6维姿态信息来填补这一空白，从而解决了这一问题。我们重点突出了这个数据集的多模态性质，并讨论和展示了它在各种下游任务中的实用性，例如地点识别和2D和3D语义分割任务。
URL
https://arxiv.org/abs/2404.18477
