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Tsukuba Challenge Datasets

Real World Datasets for Autonomous Navigation

Name Provider (Team) Type Location
map_tc21_mapiv MapIV Map Tsukuba Challenge 2021 Course
map_tc19_gnss+ins_furo fuRo Map Tsukuba Challenge 2019 Course
tc19_furo fuRo Sensor Tsukuba Challenge 2019 Course
map_tc19_furo fuRo Map Tsukuba Challenge 2019 Course
tc19_tsuchiura-pj Tsuchiura Project Sensor Tsukuba Challenge 2019 Course
tc18_furo fuRo Sensor Tsukuba Challenge 2018 Course
map_tc18_furo fuRo Map Tsukuba Challenge 2018 Course
iias_tsukuba23 Aqua (University of Tsukuba) Sensor iias Tsukuba 2023
utsukuba22_university_of_tsukuba University of Tsukuba Sensor Tsukuba campus 2022
map_utsukuba22_university_of_tsukuba University of Tsukuba Map Tsukuba campus 2022
tsudanuma20_cit Chiba Institute of Technology Sensor Tsudanuma 2020
map_tsudanuma20_cit Chiba Institute of Technology Map Tsudanuma 2020 
## Example Course Template

### Course Name, Team Name, Sensor Data / Map Data

Tsukuba Challenge 2021 Course

TC2021, MapIV, SLAM Map Data

  • Short Name: map_tc21_mapiv
  • Provider (Team): MapIV
  • Type: Map
  • Location: Tsukuba Challenge 2021 Course
  • File: 0.15_map_all.pcd (505 MB), map_converted*.pcd (10.9 GB)
  • Size: 505 MB, 10.9GB
  • Format: pcd
  • Number of Points: 31,559,485, 682,377,762
  • Point Type:
    • XYZ: Yes
    • Intensity: Yes
    • Color: No
    • Normal: No
  • SLAM Method: MapIV Engine (HESAI PandarXT-32 + Septentrio mosaic)
  • Description: The LiDAR measurement of more than 70 meters is cut off. "map_converted*.pcd" are raw point cloud maps, and "0.15_map_all.pcd" is a downsampled and concatenated map of them. We used Voxel Grid Filter for downsamapling. Map coordinate system is the Japan Plane Rectangular CS IXj, and its height is orthometric height.
  • License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

Tsukuba Challenge 2019 Course

TC2019, fuRo, GNSS+INS Map Data

TC2019, fuRo, Sensor Data

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

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_tsuchiura-pj
  • Provider (Team): Tsuchiura Project
  • Type: Sensor
  • Location: Tsukuba Challenge 2019 Course
  • 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: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

Tsukuba Challenge 2018 Course

TC2018, fuRo, Sensor Data

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

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

iias Tsukuba 2023, Aqua (University of Tsukuba), Sensor Data

UTsukuba 2022, University of Tsukuba, Sensor Data

  • Short Name: utsukuba22_university_of_tsukuba
  • Provider (Team): University of Tsukuba
  • Type: Sensor
  • Location: Tsukuba campus 2022
  • File: utsukuba22_university_of_tsukuba.bag.zst
  • Size: 8 GB
  • Format: rosbag
  • Date: 2022-07-09 10:34:13
  • Duration: 47:02s
  • Setup: Mobile Robot (Joystick Operation)
  • Sensors:
    • Lidar: Velodyne VLP-16
    • Camera: No
    • Radar: No
    • GNSS: No
    • IMU: LOAD MicroStrain 3DM-GX5-25
    • Motor Encoders (Wheel Odometry): Yes
  • Description: This bag file is compressed with a command zstd. You can decompress with the command zstd -d utsukuba22_university_of_tsukuba.bag.zst
  • License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

UTsukuba 2022, University of Tsukuba, Map Data

[WIP] Tsudanuma 2020, fuRo, Sensor Data

Tsudanuma 2020, Chiba Institute of Technology, Sensor Data

  • Short Name: tsudanuma20_cit
  • Provider (Team): Chiba Institute of Technology
  • Type: Sensor
  • Location: Tsudanuma 2020
  • File: tsudanuma20_cit_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: Creative Commons Attribution 4.0 International (CC BY 4.0)

Tsudanuma 2020, Chiba Institute of Technology, Map Data

  • Short Name: map_tsudanuma20_cit
  • Provider (Team): Chiba Institute of Technology
  • Location: Tsudanuma 2020
  • Type: Map
  • File: map_tsudanuma20_cit.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: LIO-SAM
  • Description: Tsudanuma Campus of Chiba Institute of Technology.
  • License: Creative Commons Attribution 4.0 International (CC BY 4.0)

[WIP] Related Datasets