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MIN3D

DOI:10.1007/s41064-023-00260-0 Static Badge

MultI-seNsor 3D mapping with an unmanned ground vehicle for mining applications

Robotic dataset for developing mobile mapping solutions for challenging, GNSS-denied and subterranean environments

Authors

Paweł Trybała, Jarosław Szrek, Fabio Remondino, Paulina Kujawa, Jacek Wodecki, Jan Blachowski, Radosław Zimroz

Abstract

The research potential in the field of mobile mapping technologies, particularly for specific applications, is hindered by several constraints. These include the need for costly hardware to collect data (possibly with automation using mobile platforms such as robots or drones), limited access to target sites with specific environmental conditions, and the establishment of a ground truth model for evaluating developed solutions. To address these challenges, the utilization of open datasets presents a viable solution. However, the availability of datasets that encompass truly demanding mixed indoor-outdoor and subterranean conditions is currently limited. To alleviate this issue, we propose the MIN3D dataset (MultI-seNsor 3D mapping with an unmanned ground vehicle for mining applications). This dataset was gathered using a wheeled mobile robot in two distinct locations: (i) textureless dark corridors within a university campus and (ii) tunnels of an underground site in Walim (Poland). It comprises around 150 GB of raw data, including images captured by multiple co-calibrated monocular and stereo cameras, a thermal camera, 2 LiDARs, and 3 inertial measurement units. Reliable ground truth point clouds were obtained using a survey-grade terrestrial laser scanner. By openly sharing this dataset, we aim to support the efforts of the scientific community in developing robust methods for navigation and mapping in challenging underground conditions. In the paper, we describe the collected data and provide an initial accuracy assessment of some visual- and LiDAR-based simultaneous localization and mapping (SLAM) algorithms for selected sequences. Encountered problems, open research questions and areas that could benefit from utilizing our dataset are discussed.

Main Contributions:

  • Challenging scenes in indoor and underground conditions, targeting typical issues of subterranean environments: variable illumination, featureless or textureless areas, repeating or complex geometry.
  • Simultaneously acquired data with many sensors (cameras, LiDAR scanners, IMUs) on a single robotic platform.
  • High-quality reference point clouds, acquired with a survey-grade Terrestrial Laser Scanner.

Related papers

Trybała, P., Szrek, J., Remondino, F. et al. MIN3D Dataset: MultI-seNsor 3D Mapping with an Unmanned Ground Vehicle. PFG (2023).

Trybała, P., Szrek, J., Remondino, F. et al. CALIBRATION OF A MULTI-SENSOR WHEELED ROBOT FOR THE 3D MAPPING OF UNDERGROUND MINING TUNNELS. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2/W2-2022, 135–142 (2022).

If you use our work, please cite:

@article{Trybala2023MIN3D,
  title = {MIN3D Dataset: MultI-seNsor 3D Mapping with an Unmanned Ground Vehicle},
  ISSN = {2512-2819},
  url = {http://dx.doi.org/10.1007/s41064-023-00260-0},
  DOI = {10.1007/s41064-023-00260-0},
  journal = {PFG – Journal of Photogrammetry,  Remote Sensing and Geoinformation Science},
  publisher = {Springer Science and Business Media LLC},
  author = {Trybała,  Paweł and Szrek,  Jarosław and Remondino,  Fabio and Kujawa,  Paulina and Wodecki,  Jacek and Blachowski,  Jan and Zimroz,  Radosław},
  year = {2023},
  month = oct 
}

@article{Trybala2022calibration,
  title = {CALIBRATION OF A MULTI-SENSOR WHEELED ROBOT FOR THE 3D MAPPING OF UNDERGROUND MINING TUNNELS},
  volume = {XLVIII-2/W2-2022},
  ISSN = {2194-9034},
  url = {http://dx.doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-135-2022},
  DOI = {10.5194/isprs-archives-xlviii-2-w2-2022-135-2022},
  journal = {The International Archives of the Photogrammetry,  Remote Sensing and Spatial Information Sciences},
  publisher = {Copernicus GmbH},
  author = {Trybała,  P. and Szrek,  J. and Remondino,  F. and Wodecki,  J. and Zimroz,  R.},
  year = {2022},
  month = dec,
  pages = {135–142}
}

Dataset

Sensor Setup

Sensor/Device Model Specification
Basler Stereo acA1440-220um 2x720x540 px, 15 Hz
Intel RealSense RGB D455 640x480 px, 15 Hz
Intel RealSense Stereo D455 2x640x480 px, 15 Hz
Intel RealSense Depth D455 640x480 px, 15 Hz
FLIR IR VUE-640 640x512 px, 15 Hz
LiDAR#1 Livox Horizon 10 Hz, ~20 mm accuracy individual point timestamps
LiDAR#2 Velodyne VLP-16 10 Hz, ~20 mm accuracy, actuated (180° rotation)
IMU#1 Livox 6-axis, 200 Hz
IMU#2 RealSense 6-axis, 400 Hz
IMU#3 NGIMU 6-axis, 20 Hz
GT 3D Scanner Riegl VZ-400i ~5 mm accuracy

Point clouds shared as .ply files

RGB/IR images as .png files

Depth maps: .png images, 16bit int - metric depth in millimeters

IMU data as .csv files

Ground Truth Point Clouds

Acquired with a Riegl VZ-400i TLS and registered with RiScan PRO

Data Sequences

Our dataset consists of 8 sequences in total.

University dataset

Sequence 1 2 3
Thumbnail
Features Ground floor Ground floor-outdoor loop Multiple indoor loops with varying illumination
Size [GB] 26.1 22.1 18.9
Cameras
Basler stereo uni_1_basler_left, uni_1_basler_right uni_2_basler_left, uni_2_basler_right uni_3_basler_left, uni_3_basler_right
RGB uni_1_rgb uni_2_rgb uni_3_rgb
RealSense Stereo uni_1_realsense_left, uni_1_realsense_right uni_2_realsense_left, uni_2_realsense_right uni_3_realsense_left, uni_3_realsense_right
RealSense Depth uni_1_realsense_depth uni_2_realsense_depth uni_3_realsense_depth
RealSense RGB uni_1_realsense_rgb uni_2_realsense_rgb uni_3_realsense_rgb
FLIR IR uni_1_ir uni_2_ir uni_3_ir
IMUs
Livox internal IMU uni_1_imu_livox uni_2_imu_livox uni_3_imu_livox
RealSense IMU uni_1_imu_realsense uni_2_imu_realsense uni_3_imu_realsense
NGIMU uni_1_ngimu_accelerations, uni_1_ngimu_euler_angles uni_2_ngimu_accelerations, uni_2_ngimu_euler_angles uni_3_ngimu_accelerations, uni_3_ngimu_euler_angles
LiDARs
Velodyne VLP-16 uni_1_velodyne uni_2_velodyne uni_3_velodyne
Livox uni_1_livox uni_2_livox uni_3_livox
Ground truth
Riegl VZ-400i uni_1_gt uni_2_gt uni_3_gt
Calibration data calibration

Underground dataset

Sequence 1 2 3 4 5
Features Main tunnel, forward pass Main tunnel, return pass Loop closures, complicated trajectory Lost camera problem, loop closures Side tunnel, less structured
Size [GB] 13.2 10.8 17.9 13.9 22.5
Cameras
Basler stereo und_1_basler_left, und_1_basler_right und_2_basler_left, und_2_basler_right und_3_basler_left, und_3_basler_right und_4_basler_left, und_4_basler_right und_5_basler_left, und_5_basler_right
RGB und_1_rgb und_2_rgb und_3_rgb und_4_rgb und_5_rgb
RealSense Stereo und_1_realsense_left, und_1_realsense_right und_2_realsense_left, und_2_realsense_right und_3_realsense_left, und_3_realsense_right und_4_realsense_left, und_4_realsense_right und_5_realsense_left, und_5_realsense_right
RealSense Depth und_1_realsense_depth und_2_realsense_depth und_3_realsense_depth und_4_realsense_depth und_5_realsense_depth
RealSense RGB und_1_realsense_rgb und_2_realsense_rgb und_3_realsense_rgb und_4_realsense_rgb und_5_realsense_rgb
FLIR IR und_1_ir und_2_ir und_3_ir und_4_ir und_5_ir
IMUs
Livox internal IMU und_1_imu_livox und_2_imu_livox und_3_imu_livox und_4_imu_livox und_5_imu_livox
RealSense IMU und_1_imu_realsense und_2_imu_realsense und_3_imu_realsense und_4_imu_realsense und_5_imu_realsense
NGIMU und_1_ngimu_accelerations, und_1_ngimu_euler_angles und_2_ngimu_accelerations, und_2_ngimu_euler_angles und_3_ngimu_accelerations, und_3_ngimu_euler_angles und_4_ngimu_accelerations, und_5_ngimu_euler_angles und_5_ngimu_accelerations, und_5_ngimu_euler_angles
LiDARs
Velodyne VLP-16 und_1_velodyne und_2_velodyne und_3_velodyne und_4_velodyne und_5_velodyne
Livox und_1_livox und_2_livox und_3_livox und_4_livox und_5_livox
Ground truth
Riegl VZ-400i underground_gt (save as a .las file)
Calibration data (same as University) calibration

Acknowledgements

The authors offer special thanks to Walimskie Drifts in Walim (https://sztolnie.pl) for making the facility available for data acquisition.

Funding

This work was partly supported by EIT RawMaterials GmbH within the activities of the AMICOS—Autonomous Monitoring and Control System for Mining Plants—project (Agreement No. 19018) and VOT3D—project (Agreement No. 21119). The work was also partly supported by the FAIR project, Piano Nazionale di Ripresa e Resilienza.

Contact

This dataset is provided for academic purposes. For any inquiries please contact <Paweł Trybała: [email protected]>.

License

The data provided here is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.