KUCL: Korea University Camera-LIDAR Dataset

Overview

The Korea University Camera-LIDAR (KUCL) dataset contains images and point clouds acquired in indoor and outdoor environments for various applications (e.g., calibration of rigid-body transformation between camera and LIDAR) in robotics and computer vision communities.

• Indoor dataset: contains 63 pairs of images and point clouds ('indoor.zip'). We collected the indoor dataset in a static indoor environment with walls, floor, and ceiling.
• Outdoor dataset: 61 pairs of images and point clouds ('outdoor.zip'). We collected the outdoor dataset in an outdoor environment including buildings and trees.

Setup

The images were taken using a Point Grey Ladybug5 (specifications) camera and point clouds were acquired with a Velodyne VLP-16 LIDAR (specifications). We rigidly mounted both sensors on the sensor frame during the overall data acquisition. Each pair of images and point clouds was discretely acquired while maintaining the sensor system standing still to reduce time-synchronization problems.

Description

Each dataset (zip file) is organized as follows:

• images/pano: This folder contains spherical panorama images (8000 X 4000) collected using the Ladybug5.
• images/pinhole/cam0~cam5: These folders contain rectified pinhole images (2448 X 2048) collected using six cameras (cam0~cam5) of the Ladybug5.
• images/pinhole/mask: This folder contains the mask (BW image) of each camera of the Ladybug5.
• images/pinhole/cam_param_pinhole.txt: This file contains extrinsic (transformation from the Ladybug5 to each lens) and intrinsic (focal length and center) parameters of each lens of the Ladybug5. For details of Ladybug5 coordinate system, please refer to the technical application note.
• scans: This folder contains point clouds collected using the VLP-16 LIDAR in text files. The first line of each file is the number of points (N), and the remaining lines are points and corresponding reflectivities (N X 4).

We also provide MATLAB functions projecting point cloud onto spherical panorama and pinhole images. Before running the following functions, please unzip the dataset file ('indoor.zip' or 'outdoor.zip') under the main directory.

• run_pano_projection.m: This function projects points onto a spherical panorama image. Lines 19-20 select dataset and index of an image and a point cloud.
• run_pinhole_projection.m: This function projects points onto a pinhole image. Lines 19-21 select dataset, index of an image and a point cloud, and pinhole camera index.

The rigid-body transformation between the Ladybug5 and the VLP-16 in each function is acquired using our edge-based Camera-LIDAR calibration method with Gaussian Mixture Model (GMM). For the details, please refer to our paper (https://doi.org/10.1002/rob.21893).

Citation

Please cite the following paper when using this dataset in your work.

• Jaehyeon Kang and Nakju L. Doh, "Automatic Targetless Camera-LIDAR Calibration by Aligning Edge with Gaussian Mixture Model," Journal of Field Robotics, vol. 37, no. 1, pp.158-179, 2020.
• @ARTICLE {kang-2020-jfr,
      AUTHOR = {Jaehyeon Kang and Nakju Lett Doh},
      TITLE = {Automatic Targetless Camera–{LIDAR} Calibration by Aligning Edge with {Gaussian} Mixture Model},
      JOURNAL = {Journal of Field Robotics},
      YEAR = {2020},
      VOLUME = {37},
      NUMBER = {1},
      PAGES = {158--179},
  }

License information

The KUCL dataset is released under a Creative Commons Attribution 4.0 International License, CC BY 4.0

Contact Information

If you have any issues about the KUCL dataset, please contact us at kangjae07@gmail.com.