Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments
[ICRA 2023] Introducing Wild-Places, a challenging large-scale dataset and benchmark 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.
Pre-print: https://arxiv.org/abs/2211.12732
Benchmark Website: https://csiro-robotics.github.io/Wild...
Dataset: https://doi.org/10.25919/jm05-g895
If you find this paper helpful for your research, please cite our paper using the following reference:
Joshua Knights, Kavisha Vidanapathirana, Milad Ramezani, Sridha Sridharan, Clinton Fookes, and Peyman Moghadam, "Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments." IEEE International Conference on Robotics and Automation (ICRA) (2023).