Li, Jing; Benndorf, Jörg; Köhler, Christian; Loskot, Paulina (2025): SubSurfaceGeoRobo: A Comprehensive Underground Dataset for SLAM-based Geomonitoring with Sensor Calibration [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.975532
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Published: 2025-03-26 • DOI registered: 2025-03-27
Abstract:
With the introduction of mobile mapping technologies, geomonitoring has become increasingly efficient and automated. The integration of Simultaneous Localization and Mapping (SLAM) and robotics has effectively addressed the challenges posed by many mapping or monitoring technologies, such as GNSS and unmanned aerial vehicles, which fail to work in underground environments. However, the complexity of underground environments, the high cost of research in this area, and the limited availability of experimental sites have hindered the progress of relevant research in the field of SLAM-based underground geomonitoring.
In response, we present SubSurfaceGeoRobo, a dataset specifically focused on underground environments with unique characteristics of subsurface settings, such as extremely narrow passages, high humidity, standing water, reflective surfaces, uneven illumination, dusty conditions, complex geometry, and texture less areas. This aims to provide researchers with a free platform to develop, test, and train their methods, ultimately promoting the advancement of SLAM, navigation, and SLAM-based geomonitoring in underground environments.
SubSurfaceGeoRobo was collected in September 2024 in the Freiberg silver mine in Germany using an unmanned ground vehicle equipped with a multi-sensor system, including radars, 3D LiDAR, depth and RGB cameras, IMU, and 2D laser scanners. Data from all sensors are stored as bag files, allowing researchers to replay the collected data and export it into the desired format according to their needs. To ensure the accuracy and usability of the dataset, as well as the effective fusion of sensors, all sensors have been jointly calibrated. The calibration methods and results are included as part of this dataset. Finally, a 3D point cloud ground truth with an accuracy of less than 2 mm, captured using a RIEGL scanner, is provided as a reference standard.
References:
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Riegl VZ-400i datasheet (2019). https://positics.fr/wp-content/uploads/2020/09/1.1.1-Brochure-VZ-400i.pdf
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Kim, TaeYoung; Pak, Gyuhyeon; Kim, Euntai (2024): GRIL-Calib: Targetless Ground Robot IMU-LiDAR Extrinsic Calibration Method Using Ground Plane Motion Constraints. IEEE Robotics and Automation Letters, 9(6), 5409-5416, https://doi.org/10.1109/LRA.2024.3392081
Li, Jing; Benndorf, Jörg; Trybała, Paweł (2025): Quantitative analysis of different SLAM algorithms for geo-monitoring in an underground test field. International Journal of Coal Science & Technology, 12(1), 7, https://doi.org/10.1007/s40789-025-00745-w
Ma, Tao; Liu, Zhizheng; Yan, Guohang; Li, Yikang (preprint): CRLF: Automatic Calibration and Refinement based on Line Feature for LiDAR and Camera in Road Scenes. arXiv, https://doi.org/10.48550/ARXIV.2103.04558
Wei, Pengjin; Yan, Guohang; Li, Yikang; Fang, Kun; Cai, Xinyu; Yang, Jie; Liu, Wei (2022): CROON: Automatic Multi-LiDAR Calibration and Refinement Method in Road Scene [webpage]. https://arxiv.org/abs/2203.03182
Yuan, Chongjian; Liu, Xiyuan; Hong, Xiaoping; Zhang, Fu (2021): Pixel-level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments [webpage]. https://arxiv.org/abs/2103.01627
Additional metadata:
Funding:
German Research Foundation (DFG), grant/award no. 422117092: DFG_422117092_ Mobiles Multisensorsystem
Coverage:
Latitude: 50.928000 * Longitude: 13.357000
Event(s):
Parameter(s):
License:
Creative Commons Attribution 4.0 International (CC-BY-4.0)
Status:
Curation Level: Basic curation (CurationLevelB)
Size:
54 data points
Data
All files referred to in data matrix can be downloaded in one go as ZIP or TAR. Be careful: This download can be very large! To protect our systems from misuse, we require to sign up for an user account before downloading.
| 1 Type | 2 Content | 3 Binary | 4 Binary (Size) [Bytes] | 5 Binary (Type) |
|---|---|---|---|---|
| Figures | Fig. 1 Aerial view of the Reiche Zeche mine in Freiberg, Germany | Fig1_Aerial_view_of_the_Reiche_Zeche_mine_in_Freiberg_Germany.png | 243.4 kBytes | image/png |
| Figures | Fig. 2 Example of the data collection site | Fig2_Example_of_the_data_collection_site.png | 477.7 kBytes | image/png |
| Figures | Fig. 3 The mobile robot with its sensors | Fig3_The_mobile_robot_with_its_sensors.png | 1.2 MBytes | image/png |
| Figures | Fig. 4 Dataset structure | Fig4_Dataset_structure.png | 322.8 kBytes | image/png |
| Figures | Fig. 5 Overview of data collection site | Fig5_Overview_of_data_collection_site.png | 911.3 kBytes | image/png |
| Sensor Data (Bag Files) | 01_20m_other_sensors.bag | 01_20m_other_sensors.bag | 2.6 GBytes | application/octet-stream |
| Sensor Data (Bag Files) | 02_20m_zed2.bag | 02_20m_zed2.bag | 6.1 GBytes | application/octet-stream |
| Sensor Data (Bag Files) | 03_80m_other_sensor.bag | 03_80m_other_sensor.bag | 10.9 GBytes | application/octet-stream |
| Sensor Data (Bag Files) | 04_80m_zed2.bag | 04_80m_zed2.bag | 9.1 GBytes | application/octet-stream |
| Calibration Data | 001 zed2_left_right_intrinsic.txt | 001_zed2_left_right_intrinsic.txt | 768 Bytes | text/plain |
| Calibration Data | 002 zed2_imu_intrinsic.yaml | 002_zed2_imu_intrinsic.yaml | 747 Bytes | text/plain |
| Calibration Data | 003 LITEF_imu_intrinsic.yaml | 003_LITEF_imu_intrinsic.yaml | 774 Bytes | text/plain |
| Calibration Data | 004 ptz_intrinsic.txt | 004_ptz_intrinsic.txt | 303 Bytes | text/plain |
| Calibration Data | 001 Approximate_value.txt (Measured manually in mm) | 001_Approximate_value.txt | 4.3 kBytes | text/plain |
| Calibration Data | 002 Refined_value.txt (Modelling using a handheld scanner with a precision of 0.035 mm and a resolution of 0.025 mm) | 002_Refined_value.txt | 794 Bytes | text/plain |
| Calibration Data | 003 With_software_tools.txt (Calibration using different software tools) | 003_With_software_tools.txt | 1.1 kBytes | text/plain |
| Ground Truth Data | 3D_point_cloud_GT.las | 3D_point_cloud_GT.las | 3.9 GBytes | application/octet-stream |
| Ground Truth Data | Control_point_coordinate.txt | control_point_coordinate.txt | 1.5 kBytes | text/plain |