We present a large dataset with a variety of mobile mapping sensors collected using a handheld device carried at typical walking speeds for nearly 2.2 km through New College, Oxford.

The dataset includes data from two commercially available devices:

  • Intel Realsense D435i - a stereoscopic-inertial camera
  • Ouster OS-1 64 - a multi-beam 3D LiDAR also with an IMU

The dataset is paired with precise centimetre accurate ground truth provided by a Leica BLK scanner.

Click here to get access to the data set.

new

We used a tripod-mounted survey grade LiDAR scanner to capture a detailed millimeter-accurate 3D map of the test location (containing 290 million points). Using the map we inferred centimeter-accurate 6 Degree of Freedom (DoF) ground truth for the position of the device for each LiDAR scan to enable better evaluation of LiDAR and visual localisation, mapping and reconstruction systems.

This ground truth is the particular novel contribution of this dataset and we believe that it will enable systematic evaluation which many similar datasets have lacked. The dataset combines both built environments, open spaces and vegetated areas so as to test localization and mapping systems such as vision-based navigation, visual and LiDAR SLAM, 3D LIDAR reconstruction and appearance-based place recognition.

Welcome back to New College!

Citation

If you use this dataset in your research, please cite this paper:

The Newer College Dataset: Handheld LiDAR, Inertial and Vision with Ground Truth
Milad Ramezani, Yiduo Wang, Marco Camurri, David Wisth, Matias Mattamala and Maurice Fallon [Preprint] [PDF] [Video]

@INPROCEEDINGS{ramezani2020newer,
    title={The Newer College Dataset: Handheld LiDAR, Inertial and Vision with Ground Truth},
    author={M. {Ramezani} and Y. {Wang} and M. {Camurri} and D. {Wisth} and M. {Mattamala} and M. {Fallon}},
    booktitle={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
    year={2020},
    volume={},
    number={},
    pages={},
    doi={}}
}

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License and is intended for non-commercial academic use.
If you are interested in using the dataset for commercial purposes please contact us.

gt