Sensor placement

• LiDAR: Ouster OS1-128 LiDAR sensor with 128 channels vertical resolution

• Stereo Camera: Carnegie Robotics MultiSense S21 stereo camera with grayscale, colour, and IR enhanced imagers, 2048x1088 @ 2MP resolution

• GNSS/INS: OxTS RT3000v3 global navigation satellite and inertial navigation system, supporting localization from GPS, GLONASS, BeiDou, Galileo, PPP and SBAS constellations

• Lux Meter: Yocto Light V3, a USB ambient light sensor (lux meter), measuring ambient light up to 100,000 lux

Panoramic Imagery

Reflectivity imagery

Ambient imagery

File Description

Each file contains 8 topics for each frame in DurLAR dataset,

• ambient/: panoramic ambient imagery
• reflec/: panoramic reflectivity imagery
• image_01/: right camera (grayscale+synced+rectified)
• image_02/: left RGB camera (synced+rectified)
• ouster_points: ouster LiDAR point cloud (KITTI-compatible binary format)
• gps, imu, lux: csv file format

The structure of the provided DurLAR full dataset zip file,

DurLAR_<date>/  
├── ambient/  
│   ├── data/  
│   │   └── <frame_number.png>   [ ..... ]   
│   └── timestamp.txt  
├── gps/  
│   └── data.csv  
├── image_01/  
│   ├── data/  
│   │   └── <frame_number.png>   [ ..... ]   
│   └── timestamp.txt  
├── image_02/  
│   ├── data/  
│   │   └── <frame_number.png>   [ ..... ]   
│   └── timestamp.txt  
├── imu/  
│   └── data.csv  
├── lux/  
│   └── data.csv  
├── ouster_points/  
│   ├── data/  
│   │   └── <frame_number.bin>   [ ..... ]   
│   └── timestamp.txt  
├── reflec/  
│   ├── data/  
│   │   └── <frame_number.png>   [ ..... ]   
│   └── timestamp.txt  
└── readme.md                    [ this README file ]  

The structure of the provided calibration zip file,

DurLAR_calibs/  
├── calib_cam_to_cam.txt              [ Camera to camera calibration results ]   
├── calib_imu_to_lidar.txt            [ IMU to LiDAR calibration results ]   
└── calib_lidar_to_cam.txt            [ LiDAR to camera calibration results ]   

Download the Dataset

Download the calibration files
Download the exemplar dataset (600 frames)

Access for the full dataset

You can request access to the full dataset in either of the way you choose. 您可任选以下其中任意链接申请访问完整数据集。

1. Access for the full dataset
2. 申请访问完整数据集

Reference

If you are making use of this work in any way (including our dataset and toolkits), you must please reference the following paper in any report, publication, presentation, software release or any other associated materials:

DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving Applications (Li Li, Khalid N. Ismail, Hubert P. H. Shum and Toby P. Breckon), In Int. Conf. 3D Vision, 2021. [pdf] [video][poster]

@inproceedings{li21durlar,
 author = {Li, L. and Ismail, K.N. and Shum, H.P.H. and Breckon, T.P.},
 title = {DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving Applications},
 booktitle = {Proc. Int. Conf. on 3D Vision},
 year = {2021},
 month = {December},
 publisher = {IEEE},
 keywords = {autonomous driving, dataset, high resolution LiDAR, flash LiDAR, ground truth depth, dense depth, monocular depth estimation, stereo vision, 3D},
 note = {to appear},
 category = {automotive 3Dvision},
}

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