Data

Wild-Places Dataset

Commonwealth Scientific and Industrial Research Organisation
Knights, Joshua ; Vidanapathirana, Kavisha ; Ramezani, Milad ; Sridharan, Sridha ; Fookes, Clinton ; Moghadam, Peyman
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/8p0b-vj75&rft.title=Wild-Places Dataset&rft.identifier=https://doi.org/10.25919/8p0b-vj75&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=Wild-Places is a large-scale LiDAR dataset for inter and intra-run place recognition in unstructured natural environments. The data was collected in two environments at the Karawatha and Venman walking trails in Brisbane, Australia over fourteen months, allowing for research into both long and short-term revisits. We release sub-maps produced from a global point cloud for four sequences in both environments for a total of ~67K submaps, with accurate 6DoF poses and timestamps for each submap. Lineage: The data was collected using a handheld sensor payload consisting of a spinning lidar sensor mounted at an angle of 45 degrees to maximise field of view, a motor, encoder, an IMU, and four cameras. For each collected sequence we use the Wildcat slam system to create an accurate 6DoF estimation of the pose of the sensor and to process the lidar data into a globally registered map, from which we produce our submaps. &rft.creator=Knights, Joshua &rft.creator=Vidanapathirana, Kavisha &rft.creator=Ramezani, Milad &rft.creator=Sridharan, Sridha &rft.creator=Fookes, Clinton &rft.creator=Moghadam, Peyman &rft.date=2025&rft.edition=v7&rft_rights=Creative Commons Attribution Noncommercial-Share Alike 4.0 Licence https://creativecommons.org/licenses/by-nc-sa/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2022.&rft_subject=Lidar&rft_subject=Navigation&rft_subject=Place Recognition&rft_subject=Robotics&rft_subject=AI&rft_subject=Machine Learning&rft_subject=Natural&rft_subject=Dataset&rft_subject=Autonomous vehicle systems&rft_subject=Control engineering, mechatronics and robotics&rft_subject=ENGINEERING&rft_subject=Artificial intelligence not elsewhere classified&rft_subject=Artificial intelligence&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft_subject=Machine learning not elsewhere classified&rft_subject=Machine learning&rft.type=dataset&rft.language=English Access the data
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CC-BY-NC-SA

Creative Commons Attribution Noncommercial-Share Alike 4.0 Licence
https://creativecommons.org/licenses/by-nc-sa/4.0/

Data is accessible online and may be reused in accordance with licence conditions

All Rights (including copyright) CSIRO 2022.

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Wild-Places is a large-scale LiDAR dataset for inter and intra-run place recognition in unstructured natural environments. The data was collected in two environments at the Karawatha and Venman walking trails in Brisbane, Australia over fourteen months, allowing for research into both long and short-term revisits. We release sub-maps produced from a global point cloud for four sequences in both environments for a total of ~67K submaps, with accurate 6DoF poses and timestamps for each submap.
Lineage: The data was collected using a handheld sensor payload consisting of a spinning lidar sensor mounted at an angle of 45 degrees to maximise field of view, a motor, encoder, an IMU, and four cameras. For each collected sequence we use the Wildcat slam system to create an accurate 6DoF estimation of the pose of the sensor and to process the lidar data into a globally registered map, from which we produce our submaps.

Available: 2025-12-12

Data time period: 2021-06-11 to 2022-08-12

This dataset is part of a larger collection

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Subjects
AI | Artificial Intelligence | Artificial Intelligence Not Elsewhere Classified | Autonomous Vehicle Systems | Control Engineering, Mechatronics and Robotics | Dataset | Engineering | Information and Computing Sciences | Lidar | Machine Learning | Machine Learning | Machine Learning Not Elsewhere Classified | Natural | Navigation | Place Recognition | Robotics |

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Identifiers
ACN 633 798 857