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

Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments