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UrbanNav

An Open-Sourcing Localization Dataset Collected in Asian Urban Canyons, including Tokyo and Hong Kong

This repository is the usage page of the UrbanNav dataset. Positioning and localization in deep urban canyons using low-cost sensors is still a challenging problem. The accuracy of GNSS can be severely challenged in urban canyons due to the high-rising buildings, leading to numerous Non-line-of-sight (NLOS) receptions and multipath effects. Moreover, the excessive dynamic objects can also distort the performance of LiDAR, and camera. The UrbanNav dataset wishes to provide a challenging data source to the community to further accelerate the study of accurate and robust positioning in challenging urban canyons. The dataset includes sensor measurements from GNSS receiver, LiDAR, camera and IMU, together with accurate ground truth from SPAN-CPT system. Different from the existing dataset, such as Waymo, KITTI, UrbanNav provide raw GNSS RINEX data. In this case, users can improve the performance of GNSS positioning via raw data. In short, the UrbanNav dataset pose a special focus on improving GNSS positioning in urban canyons, but also provide sensor measurements from LiDAR, camera and IMU. If you got any problems when using the dataset and cannot find a satisfactory solution in the issue list, please open a new issue and we will reply ASAP.

Key words: Positioning, Localization, GNSS Positioning, Urban Canyons, GNSS Raw Data,Dynamic Objects, GNSS/INS/LiDAR/Camera, Ground Truth

Important Notes:

  • About access to GNSS RINEX file: The GNSS measurements is provided as GNSS RINEX data. We will recently open-source a package, the GraphGNSSLib, which provide easy access to the GNSS RINEX file and publish the data as customized ROS message. Meanwhile, we GraphGNSSLib also provide the capabilities of GNSS positioning and real-time kinematic (RTK) using factor graph optimization (FGO). If you wish to use the GraphGNSSLib, keep an eye on the update of this repo.
  • Dataset contribution: Researches who wish to contribute their dataset as part of the UrbanNav dataset, please feel free to contact us via email [email protected], [email protected], and [email protected]. We wish the UrbanNav can be a platform for navigation solution development, validation and sharing.
  • Algorithm validation and contribution: Researches are welcomed to share their navigation solution results, source code to the UrbanNav dataset after a code review process, e,g, code for GNSS/INS integration or LiDAR SLAM, etc.

Overview

Objective of the Dataset

  • Open-sourcing positioning sensor data, including GNSS, INS, LiDAR and cameras collected in Asian urban canyons;

  • Raising the awareness of the urgent navigation requirement in highly-urbanized areas, especially in Asian-Pacific regions;

  • Providing an integrated online platform for data sharing to facilitate the development of navigation solutions of the research community; and

  • Benchmarking positioning algorithms based on the open-sourcing data.

Contact Authors (corresponding to issues and maintenance of the currently available Hong Kong dataset): Li-Ta Hsu, Weisong Wen, Feng Huang, Hoi-Fung Ng, GuoHao Zhang, Xiwei Bai from the Intelligent Positioning and Navigation Laboratory, The Hong Kong Polytechnique University

Related Papers:

  • Hsu, Li-Ta, Kubo, Nobuaki, Wen, Weisong, Chen, Wu, Liu, Zhizhao, Suzuki, Taro, Meguro, Junichi, "UrbanNav:An Open-Sourced Multisensory Dataset for Benchmarking Positioning Algorithms Designed for Urban Areas," Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021), St. Louis, Missouri, September 2021, pp. 226-256. https://doi.org/10.33012/2021.17895

Hong Kong Dataset

Sensor Setups

The platform for data collection in Hong Kong is a Honda Fit. The platform is equipped with the following sensors:

DataSets

Total Size Path length Sensors Urban Canyon Download 3D PointCloud
UrbanNav-HK-Medium-Urban-1 33.7 GB (785s) 3.64 Km LiDARs/Stereo Camera/IMU/GNSS Medium ROS, GNSS, IMU, Ground Truth Medium Urban Map
UrbanNav-HK-Deep-Urban-1 63.9 GB (1536s) 4.51 Km LiDARs/Stereo Camera/IMU/GNSS Deep ROS, GNSS, IMU, Ground Truth Deep Urban Map
UrbanNav-HK-Harsh-Urban-1 147 GB (3367s) 4.86 Km LiDARs/Stereo Camera/IMU/GNSS Harsh ROS, GNSS, IMU, Ground Truth Harsh Urban Map
UrbanNav-HK-Tunnel-1 17 GB (398s) 3.15 Km LiDARs/Stereo Camera/IMU/GNSS N/A ROS, GNSS, IMU, Ground Truth Tunnel map
(Pilot data) UrbanNav-HK-Data20190428 42.9 GB (487s) 2.01 Km LiDAR/Camera/IMU/GNSS Medium ROS, GNSS N/A
(Pilot data) UrbanNav-HK-Data20200314 27.0 GB (300s) 1.21 Km LiDAR/Camera/IMU/GNSS Light ROS, GNSS N/A

UrbanNav-HK-Medium-Urban-1

Dataset UrbanNav-HK-Medium-Urban-1 is collected in a typical urban canyon of Hong Kong near TST which involves high-rising buildings, numerous dynamic objects. A updated version to UrbanNav-HK-Data20190428, two loops included. The coordinates transformation between multiple sensors, and intrinsic measurements of camera can be found via Extrinsic Parameters, IMU Nosie and Intrinsic Parameters of Camera.

UrbanNav-HK-Deep-Urban-1

Dataset UrbanNav-HK-Deep-Urban-1 is collected in a highly urbanized area of Hong Kong which involves dense traffic, small tunnels and loops. The coordinates transformation between multiple sensors, and intrinsic measurements of camera can be found via Extrinsic Parameters, IMU Nosie and Intrinsic Parameters of Camera.

UrbanNav-HK-Harsh-Urban-1

Dataset UrbanNav-HK-Harsh-Urban-1 is collected in an ultra-dense urban canyon of Hong Kong which involves dense vehicles, pedestrians and loops. The coordinates transformation between multiple sensors, and intrinsic measurements of camera can be found via Extrinsic Parameters, IMU Nosie and Intrinsic Parameters of Camera.

UrbanNav-HK-Tunnel-1

UrbanNav-HK-Tunnel-1 is collected in a sea tunnel of Hong Kong which involves dense vehicles and GNSS signal losses. The coordinates transformation between multiple sensors, and intrinsic measurements of camera can be found via Extrinsic Parameters, IMU Nosie and Intrinsic Parameters of Camera.

UrbanNav-HK-Data20190428

Brief: Dataset UrbanNav-HK-Data20190428 is collected in a typical urban canyon of Hong Kong near TST which involves high-rising buildings, numerous dynamic objects. The coordinates transformation between multiple sensors, and intrinsic measurements of camera can be found via Extrinsic Parameters, IMU Nosie and Intrinsic Parameters of Camera.

Some key features are as follows:

Date of Collection Total Size Path length Sensors
2019/04/28 42.9 GB 2.01 Km GNSS/LiDAR/Camera/IMU/SPAN-CPT
  • Download by Dropbox Link: Data INFO
    • UrbanNav-HK-Data20190428 (ROS)
      • ROSBAG file which includes:
        • GNSS positioning (solution directly from GNSS receiver): /ublox_node/fix
        • 3D LiDAR point clouds: /velodyne_points
        • Camera: /camera/image_color
        • IMU: /imu/data
        • SPAN-CPT: /novatel_data/inspvax
    • GNSS (RINEX)
      • GNSS RINEX files, to use it, we suggest to use the RTKLIB
    • IMU/SPAN-CPT (CSV)
      • IMU and SPAN-CPT data for non-ROS users.

For mainland china users, please download the dataset using the Baidu Clouds Links

  • Download by Baidu Cloud Link: Data INFO, (qm3l)
    • UrbanNav-HK-Data20190428 (ROS) (nff4)
      • ROSBAG file whihc includes:
        • GNSS positioning (solution directly from GNSS receiver): /ublox_node/fix
        • 3D LiDAR point clouds: /velodyne_points
        • Camera: /camera/image_color
        • IMU: /imu/data
        • SPAN-CPT: /novatel_data/inspvax
    • GNSS (RINEX) (gojb)
      • GNSS RINEX files, to use it, we suggest to use the RTKLIB
    • IMU/SPAN-CPT (CSV) (k3dz)
      • IMU and SPAN-CPT data for non-ROS users.

UrbanNav-HK-Data20200314

Brief: Dataset UrbanNav-HK-Data2020314 is collected in a low-urbanization area in Kowloon which suitable for algorithmic verification and comparison. The coordinates transformation between multiple sensors, and intrinsic measurements of camera can be found via Extrinsic Parameters, IMU Nosie and Intrinsic Parameters of Camera.

Some key features are as follows:

Date of Collection Total Size Path length Sensors
2020/03/14 27.0 GB 1.21 Km LiDAR/Camera/IMU/SPAN-CPT
  • Download by Dropbox Link:
    • UrbanNav-HK-Data20200314 (ROS)
      • ROSBAG file which includes:
        • 3D LiDAR point clouds: /velodyne_points
        • Camera: /camera/image_color
        • IMU: /imu/data
        • SPAN-CPT: /novatel_data/inspvax
    • GNSS (RINEX)
      • GNSS RINEX files, to use it, we suggest to use the RTKLIB

For mainland china users, please download the dataset using the Baidu Clouds Links

  • Download by Baidu Cloud Link:
    • UrbanNav-HK-Data20200314 (ROS) (n71w)
      • ROSBAG file whihc includes:
        • 3D LiDAR point clouds: /velodyne_points
        • Camera: /camera/image_color
        • IMU: /imu/data
        • SPAN-CPT: /novatel_data/inspvax
    • GNSS (z8vw) (RINEX)
      • GNSS RINEX files, to use it, we suggest to use the RTKLIB

Tokyo Dataset

Sensor Setups

The platform for data collection in Tokyo is a Toyota Rush. The platform is equipped with the following sensors:

Dataset 1: UrbanNav-TK-20181219

Important Notes: the LiDAR calibration file for the LiDAR sensor, extrinsic parameters between sensors are not available now. If you wish to study the GNSS/LiDAR/IMU integration, we suggest using the dataset above collected in Hong Kong. However, the GNSS dataset from Tokyo is challenging which is collected in challenging urban canyons!

Date of Collection Total Size Path length Sensors
2018/12/19 4.14 GB >10 Km GNSS/LiDAR/IMU/Ground Truth

  • Download by Dropbox Link: For mainland china users, please download the dataset using the Baidu Clouds Links. Baidu Clouds Links (7xpo)

  • The dataset contains data from two runs, /Odaiba and /Shinjuku .

  • The following files are included in each dataset.

    • rover_ublox.obs and rover_trimble.obs: Rover GNSS RINEX files (5 Hz / 10 Hz)
    • imu.csv: CSV file which includes GPS time, Angular velocity, and acceleration, (50 Hz)
    • lidar.bag: ROSBAG file which includes LiDAR data /velodyne_packets
    • base_trimble.obs and base.nav: GNSS RINEX files of base station (1 Hz)
    • reference.csv: Ground truth from Applanix POS LV620 (10 Hz)

  • The travel trajectory of /Odaiba

  • The travel trajectory of /Shinjuku

Acknowledgements

We acknowledge the help from Yihan Zhong, Jiachen Zhang, Yin-chiu Kan, Weichang Xu and Song Yang.

License

For any technical issues, please contact Feng Huang via email [email protected] and Weisong Wen via email [email protected]. For collaboration inquiries, please contact Li-Ta Hsu via email [email protected].