taroz / tc-datasets

Real World Datasets for Autonomous Navigation

Tsukuba Challenge Datasets

Real World Datasets for Autonomous Navigation

Tsukuba Challenge 2019 Course

TC2019, fuRo, Sensor Data

• Short Name: tc19_furo
• File: tc19_js_2019-09-14-14-12-15.bag (56 GB), tc19_js_2019-09-14-14-12-15.bag.7z (22 GB)
• Size: 55.8 GB
• Format: rosbag
• Date: 2019-09-14 14:12:15
• Duration: 1hr 47:31s
• Setup: Differential Wheeled Robot (Joystick Operation)
• Sensors:
  • Lidar: SureStar R-Fans-16M
  • Camera: No
  • Radar: No
  • GNSS: No
  • IMU: Xsens MTi-3
  • Motor Encoders (Wheel Odometry): Yes
• Description: Low cost 3D-Lidar. Low accuracy wheel odometry.
• License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

If you use our dataset in your academic work, please cite the following paper [DOI]:

Yoshitaka Hara and Masahiro Tomono: "Moving Object Removal and Surface Mesh Mapping for Path Planning on 3D Terrain", Advanced Robotics, vol. 34, no. 6, pp. 375--387, 2020.

TC2019, fuRo, Map Data

• Short Name: map_tc19_furo
• File: map_tc19_o085_f-04_t05.pcd
• Size: 683 MB
• Format: pcd
• Number of Points: 22,356,688
• Point Type:
  • XYZ: Yes
  • Intensity: Yes
  • Color: No
  • Normal: Yes
• SLAM Method: Occupancy Voxel Mapping using 3D Cartographer
• Description: Moving objects have been removed.
• License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

If you use our dataset in your academic work, please cite the following paper [DOI]:

Yoshitaka Hara and Masahiro Tomono: "Moving Object Removal and Surface Mesh Mapping for Path Planning on 3D Terrain", Advanced Robotics, vol. 34, no. 6, pp. 375--387, 2020.

TC2019, Tsuchiura Project, Sensor Data

• Short Name: tc19_tsuchiuraPJ
• File: 2019-11-10-13-37-16.bag
• Size: 12.8 GB
• Format: rosbag
• Date: 2019-11-10 13:37:16
• Duration: 53:18s
• Setup: Mobile Robot (Autonomous Operation)
• Sensors:
  • Lidar: Hokuyo YVT-X002, UTM-30LX-EW, URM-40LC-EW
  • Camera: Ricoh Theta S, logicool C920
  • Radar: No
  • GNSS: u-blox NEO-M8T
  • IMU: No
  • Motor Encoders (Wheel Odometry): Yes
• Description: This bag file is compressed with 7z.
• License: TBD

Tsukuba Challenge 2018 Course

TC2018, fuRo, Sensor Data

• Short Name: tc18_furo
• File: tc18_js_2018-09-15-14-31-29.bag (41 GB), tc18_js_2018-09-15-14-31-29.bag.7z (12 GB)
• Size: 40.8 GB
• Format: rosbag
• Date: 2018-09-15 14:31:29 (Converted to rosbag on 2018-09-20)
• Duration: 1hr 31:43s
• Setup: Differential Wheeled Robot (Joystick Operation)
• Sensors:
  • Lidar: Velodyne VLP-16
  • Camera: No
  • Radar: No
  • GNSS: No
  • IMU: Xsens MTi-3
  • Motor Encoders (Wheel Odometry): Yes
• Description: High accuracy wheel odometry.
• License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

If you use our dataset in your academic work, please cite the following paper [DOI]:

Yoshitaka Hara and Masahiro Tomono: "Moving Object Removal and Surface Mesh Mapping for Path Planning on 3D Terrain", Advanced Robotics, vol. 34, no. 6, pp. 375--387, 2020.

TC2018, fuRo, Map Data

• Short Name: map_tc18_furo
• File: map_tc18_o085_f-04_t30.pcd
• Size: 519 MB
• Format: pcd
• Number of Points: 17,002,094
• Point Type:
  • XYZ: Yes
  • Intensity: Yes
  • Color: No
  • Normal: Yes
• SLAM Method: Occupancy Voxel Mapping using 3D Cartographer
• Description: Moving objects have been removed.
• License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

If you use our dataset in your academic work, please cite the following paper [DOI]:

Yoshitaka Hara and Masahiro Tomono: "Moving Object Removal and Surface Mesh Mapping for Path Planning on 3D Terrain", Advanced Robotics, vol. 34, no. 6, pp. 375--387, 2020.

Other Courses

[WIP] Tsudanuma 2020, fuRo, Sensor Data

Tsudanuma 2020, Chiba Institute of Technology, Sensor Data

• Short Name: CIT_Tsudanuma
• File: CIT_2020_compressed.bag
• Size: 57 GB
• Format: rosbag
• Date: 2020-08-27 17:43:12
• Duration: 56:12s
• Setup: Mobile Robot (Joystick Operation)
• Sensors:
  • Lidar: Velodyne VLP-16
  • Camera: Intel Realsense d435i (without depth)
  • Radar: No
  • GNSS: Drogger DG-PRO1RW (Independent Positioning)
  • IMU: Analog Devices ADIS16465
  • Motor Encoders (Wheel Odometry): Yes
• Description: This bag file is compressed with a command rosbag compress.
• License: TBD

Tsudanuma 2020, Chiba Institute of Technology, Map Data

• Short Name: map_tsudanuma
• File: map_tsudanuma.pcd
• Size: 490.8 MB
• Format: pcd
• Number of Points: 13,583,284
• Point Type:
  • XYZ: Yes
  • Intensity: Yes
  • Color: No
  • Normal: Yes
• SLAM Method: Occupancy Voxel Mapping using LIO-SAM.
• Description: Tsudanuma Campus of Chiba Institute of Technology.
• License: TBD

Example Course Template

Course Name, Team Name, Sensor Data / Map Data

[WIP] Related Datasets