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https://github.com/l1997i/DurLAR
(3DV 2021) A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving Applications
https://github.com/l1997i/DurLAR
3dvision autonomous-driving autonomous-vehicles computer-vision dataset depth-estimation durham durlar kitti lidar point-cloud robotics
Last synced: 25 days ago
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(3DV 2021) A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving Applications
- Host: GitHub
- URL: https://github.com/l1997i/DurLAR
- Owner: l1997i
- License: mit
- Created: 2021-08-02T10:21:09.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-09-07T14:36:52.000Z (3 months ago)
- Last Synced: 2024-09-07T16:06:54.060Z (3 months ago)
- Topics: 3dvision, autonomous-driving, autonomous-vehicles, computer-vision, dataset, depth-estimation, durham, durlar, kitti, lidar, point-cloud, robotics
- Homepage:
- Size: 20.7 MB
- Stars: 29
- Watchers: 2
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-3D-LiDAR-Datasets - DurLAR - 128|No|Depth|Large| (Summary Table / Update: 2023-07-12)
README
[](https://durham-repository.worktribe.com/output/1138941/durlar-a-high-fidelity-128-channel-lidar-dataset-with-panoramic-ambient-and-reflectivity-imagery-for-multi-modal-autonomous-driving-applications)
[](https://arxiv.org/abs/2406.10068)
[](https://github.com/l1997i/lim3d/blob/main/LICENSE)
# DurLAR: A High-Fidelity 128-Channel LiDAR Dataset
https://github.com/l1997i/DurLAR/assets/35445094/2c6d4056-a6de-4fad-9576-693efe2860f0
## News
- [2024/09/07] We provide the **OneDrive link** for downloading the DurLAR dataset. To access the OneDrive link, please email your affiliation, name, and intended usage to [email protected].
## Sensor placement
- **LiDAR**: [Ouster OS1-128 LiDAR sensor](https://ouster.com/products/os1-lidar-sensor/) with 128 channels vertical resolution
- **Stereo** Camera: [Carnegie Robotics MultiSense S21 stereo camera](https://carnegierobotics.com/products/multisense-s21/) with grayscale, colour, and IR enhanced imagers, 2048x1088 @ 2MP resolution
- **GNSS/INS**: [OxTS RT3000v3](https://www.oxts.com/products/rt3000-v3/) global navigation satellite and inertial navigation system, supporting localization from GPS, GLONASS, BeiDou, Galileo, PPP and SBAS constellations
- **Lux Meter**: [Yocto Light V3](http://www.yoctopuce.com/EN/products/usb-environmental-sensors/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_/
├── ambient/
│ ├── data/
│ │ └── [ ..... ]
│ └── timestamp.txt
├── gps/
│ └── data.csv
├── image_01/
│ ├── data/
│ │ └── [ ..... ]
│ └── timestamp.txt
├── image_02/
│ ├── data/
│ │ └── [ ..... ]
│ └── timestamp.txt
├── imu/
│ └── data.csv
├── lux/
│ └── data.csv
├── ouster_points/
│ ├── data/
│ │ └── [ ..... ]
│ └── timestamp.txt
├── reflec/
│ ├── data/
│ │ └── [ ..... ]
│ └── 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 ]
```
## Get Started
- [Download the **calibration files**](https://github.com/l1997i/DurLAR/raw/main/DurLAR_calibs.zip)
- [Download the **calibration files** (v2, targetless)](https://github.com/l1997i/DurLAR/raw/main/DurLAR_calibs_v2.zip)
- [Download the **exemplar ROS bag** (for targetless calibration)](https://durhamuniversity-my.sharepoint.com/:f:/g/personal/mznv82_durham_ac_uk/Ei28Yy-Gb_BKoavvJ6R_jLcBfTZ_xM5cZhEFgMFNK9HhyQ?e=rxPgI9)
- [Download the **exemplar dataset** (600 frames)](https://collections.durham.ac.uk/collections/r2gq67jr192)
- [Download the **full dataset**](https://github.com/l1997i/DurLAR?tab=readme-ov-file#access-for-the-full-dataset) (Fill in the form to request access to the full dataset)
> Note that [we did not include CSV header information](https://github.com/l1997i/DurLAR/issues/9) in the [**exemplar dataset** (600 frames)](https://collections.durham.ac.uk/collections/r2gq67jr192). You can refer to [Header of csv files](https://github.com/l1997i/DurLAR?tab=readme-ov-file#header-of-csv-files) to get the first line of the `csv` files.
> **calibration files** (v2, targetless): Following the publication of the proposed DurLAR dataset and the corresponding paper, we identify a more advanced [targetless calibration method](https://github.com/koide3/direct_visual_lidar_calibration) ([#4](https://github.com/l1997i/DurLAR/issues/4)) that surpasses the LiDAR-camera calibration technique previously employed. We provide [**exemplar ROS bag**](https://durhamuniversity-my.sharepoint.com/:f:/g/personal/mznv82_durham_ac_uk/Ei28Yy-Gb_BKoavvJ6R_jLcBfTZ_xM5cZhEFgMFNK9HhyQ?e=rxPgI9) for [targetless calibration](https://github.com/koide3/direct_visual_lidar_calibration), and also corresponding [calibration results (v2)](https://github.com/l1997i/DurLAR/raw/main/DurLAR_calibs_v2.zip). Please refer to [Appendix (arXiv)](https://arxiv.org/pdf/2406.10068) for more details.
### Access to the full dataset
Access to the complete DurLAR dataset can be requested through **one** of the following ways. 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](https://forms.gle/ZjSs3PWeGjjnXmwg9)
[2. 申请访问完整数据集](https://wj.qq.com/s2/9459309/4cdd/)
[3. OneDrive link](https://luisweb-my.sharepoint.com/:f:/g/personal/i_luisli_org/Eh7F34jln5tIgVnVAkzZZ_sBX8OgicgyeQ-1j5wzwYMFfw?e=b4hrrV). If you need access to the DurLAR dataset, please send your affiliation, name, and intended usage to [email protected], and we will provide the access password.
### Usage of the downloading script
Upon completion of the form, the download script `durlar_download` and accompanying instructions will be **automatically** provided. The DurLAR dataset can then be downloaded via the command line.
For the first use, it is highly likely that the `durlar_download` file will need to be made
executable:
``` bash
chmod +x durlar_download
```
By default, this script downloads the small subset for simple testing. Use the following command:
```bash
./durlar_download
```
It is also possible to select and download various test drives:
```
usage: ./durlar_download [dataset_sample_size] [drive]
dataset_sample_size = [ small | medium | full ]
drive = 1 ... 5
```
Given the substantial size of the DurLAR dataset, please download the complete dataset
only when necessary:
```bash
./durlar_download full 5
```
Throughout the entire download process, it is important that your network remains
stable and free from any interruptions. In the event of network issues, please delete all
DurLAR dataset folders and rerun the download script. Currently, our script supports
only Ubuntu (tested on Ubuntu 18.04 and Ubuntu 20.04, amd64). For downloading the
DurLAR dataset on other operating systems, please refer to [Durham Collections](https://collections.durham.ac.uk/collections/r2gq67jr192) for instructions.
## CSV format for `imu`, `gps`, and `lux` topics
### Format description
Our `imu`, `gps`, and `lux` data are all in `CSV` format. The **first row** of the `CSV` file contains headers that **describe the meaning of each column**. Taking `imu` csv file for example (only the first 9 rows are displayed),
1. `%time`: Timestamps in Unix epoch format.
2. `field.header.seq`: Sequence numbers.
3. `field.header.stamp`: Header timestamps.
4. `field.header.frame_id`: Frame of reference, labeled as "gps".
5. `field.orientation.x`: X-component of the orientation quaternion.
6. `field.orientation.y`: Y-component of the orientation quaternion.
7. `field.orientation.z`: Z-component of the orientation quaternion.
8. `field.orientation.w`: W-component of the orientation quaternion.
9. `field.orientation_covariance0`: Covariance of the orientation data.
### Header of `csv` files
The first line of the `csv` files is shown as follows.
For the GPS,
```csv
time,field.header.seq,field.header.stamp,field.header.frame_id,field.status.status,field.status.service,field.latitude,field.longitude,field.altitude,field.position_covariance0,field.position_covariance1,field.position_covariance2,field.position_covariance3,field.position_covariance4,field.position_covariance5,field.position_covariance6,field.position_covariance7,field.position_covariance8,field.position_covariance_type
```
For the IMU,
```
time,field.header.seq,field.header.stamp,field.header.frame_id,field.orientation.x,field.orientation.y,field.orientation.z,field.orientation.w,field.orientation_covariance0,field.orientation_covariance1,field.orientation_covariance2,field.orientation_covariance3,field.orientation_covariance4,field.orientation_covariance5,field.orientation_covariance6,field.orientation_covariance7,field.orientation_covariance8,field.angular_velocity.x,field.angular_velocity.y,field.angular_velocity.z,field.angular_velocity_covariance0,field.angular_velocity_covariance1,field.angular_velocity_covariance2,field.angular_velocity_covariance3,field.angular_velocity_covariance4,field.angular_velocity_covariance5,field.angular_velocity_covariance6,field.angular_velocity_covariance7,field.angular_velocity_covariance8,field.linear_acceleration.x,field.linear_acceleration.y,field.linear_acceleration.z,field.linear_acceleration_covariance0,field.linear_acceleration_covariance1,field.linear_acceleration_covariance2,field.linear_acceleration_covariance3,field.linear_acceleration_covariance4,field.linear_acceleration_covariance5,field.linear_acceleration_covariance6,field.linear_acceleration_covariance7,field.linear_acceleration_covariance8
```
For the LUX,
```csv
time,field.header.seq,field.header.stamp,field.header.frame_id,field.illuminance,field.variance
```
### To process the `csv` files
To process the `csv` files, you can use multiple ways. For example,
**Python**: Use the pandas library to read the CSV file with the following code:
```python
import pandas as pd
df = pd.read_csv('data.csv')
print(df)
```
**Text Editors**: Simple text editors like `Notepad` (Windows) or `TextEdit` (Mac) can also open `CSV` files, though they are less suited for data analysis.
## Folder \#Frame Verification
For easy verification of folder data and integrity, we provide the number of frames in each drive folder, as well as the [MD5 checksums](https://collections.durham.ac.uk/collections/r2gq67jr192?utf8=%E2%9C%93&cq=MD5&sort=) of the zip files.
| Folder | # of Frames |
|----------|-------------|
| 20210716 | 41993 |
| 20210901 | 23347 |
| 20211012 | 28642 |
| 20211208 | 26850 |
| 20211209 | 25079 |
|**total** | **145911** |
---
## 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](https://dro.dur.ac.uk/34293/)
(Li Li, Khalid N. Ismail, Hubert P. H. Shum and Toby P. Breckon), In Int. Conf. 3D Vision, 2021. [[pdf](https://www.l1997i.com/assets/pdf/li21durlar_arxiv_compressed.pdf)] [[video](https://youtu.be/1IAC9RbNYjY)][[poster](https://www.l1997i.com/assets/pdf/li21durlar_poster_v2_compressed.pdf)]
```
@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},
category = {automotive 3Dvision},
}
```
---