  Many existing datasets for lidar place recognition are solely representative
of structured urban environments, and have recently been saturated in
performance by deep learning based approaches. Natural and unstructured
environments present many additional challenges for the tasks of long-term
localisation but these environments are not represented in currently available
datasets. To address this we introduce Wild-Places, a challenging large-scale
dataset for lidar place recognition in unstructured, natural environments.
Wild-Places contains eight lidar sequences collected with a handheld sensor
payload over the course of fourteen months, containing a total of 63K
undistorted lidar submaps along with accurate 6DoF ground truth. Our dataset
contains multiple revisits both within and between sequences, allowing for both
intra-sequence (i.e. loop closure detection) and inter-sequence (i.e.
re-localisation) place recognition. We also benchmark several state-of-the-art
approaches to demonstrate the challenges that this dataset introduces,
particularly the case of long-term place recognition due to natural
environments changing over time. Our dataset and code will be available at
https://csiro-robotics.github.io/Wild-Places.
