  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.
