https://github.com/IMRL/UMAD
[IROS 2024] UMAD: University of Macau Anomaly Detection Benchmark Dataset
https://github.com/IMRL/UMAD
Last synced: 4 months ago
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[IROS 2024] UMAD: University of Macau Anomaly Detection Benchmark Dataset
- Host: GitHub
- URL: https://github.com/IMRL/UMAD
- Owner: IMRL
- License: mit
- Created: 2024-03-21T07:27:57.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-26T07:47:35.000Z (4 months ago)
- Last Synced: 2024-12-26T08:27:27.926Z (4 months ago)
- Language: C++
- Homepage:
- Size: 64.3 MB
- Stars: 34
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
UMAD: University of Macau Anomaly Detection Benchmark Dataset
IROS, 2024.
Dong Li,
Lineng Chen,
Cheng-Zhong Xu,
Hui Kong†
University of Macau
†Corresponding Authors
[](https://www.youtube.com/watch?v=xORb4H-AyNw "UMAD: University of Macau Anomaly Detection Benchmark Dataset")
## [UMAD Dataset Google Drive Link, about 10GB](https://drive.google.com/drive/folders/1UmZ3vA1cOunB-2wgz8T1fJDebhb-gmax), and [UMAD-Dataset-Usage-Guide-Doc](https://github.com/IMRL/UMAD/blob/main/Doc/UMAD-Dataset-Usage-Guide-Doc.md)
## 😊News
This work is maintaining. You can hit the **STAR** and **WATCH** to follow the updates.
- **2024-9-5**: We have released the UMAD-1.0 dataset, along with the robot system code.
- **2024-8-27**: We will update the **UMAD-homo-eva dataset** and the extension experiments on the [UMAD-homo-eva](https://github.com/IMRL/UMAD/blob/main/Doc/UMAD-homo-eva-dataset.md).
- **2024-8-22:** UMAD paper sharing on arXiv~
- **2024/6/30**: **UMAD** has been accepted by **IROS 2024**! Thanks to everyone who participated in this project!
- **2024/3/21**: We have publicly released a supplementary video for the paper submission.
## 📝ToDo List
- [x] Make the project paper publicly available.
- [x] Open-source the UMAD dataset.
- [x] Open-source the UMAD-homo-eval dataset.
- [ ] Open-source the code related to the datasets.
- [x] Open source robotic system code.
- [ ] Release C++/python Adaptive Warping code.
## Dataset
#### Dataset Overview
You can refer to the [UMAD-Dataset-Usage-Guide-Doc](https://github.com/IMRL/UMAD/blob/main/Doc/UMAD-Dataset-Usage-Guide-Doc.md) for information on how to use the UMAD dataset and details about the ground truth mask files.
## Benchmark
#### Anomaly Detection Benchmark
#### Change Detection Benchmark
## System
You can easily collect data or deploy a system like our **UMAD robot system**:
```bash
# Prerequisites: [FAST_LIO](https://github.com/hku-mars/FAST_LIO) and [FAST_LIO_LOCALIZATION](https://github.com/HViktorTsoi/FAST_LIO_LOCALIZATION)
# 1. Build develop environment: Download UMAD's code, and put src/FAST_LIO_LOCALIZATION in the workspace of ROS
git clone https://github.com/IMRL/UMAD
#catkin make
# 2. Build map and record path: Put the robot in the scene, run FAST_LIO and record the waypoints
roslaunch fast_lio mapping_mid360.launch
python3 UMAD/robot_system_code/script/path_record.py # generate a path.txt file
# 3.Control the robot around the environment
# 4.Shut down the fast_lio and waypoint_record scripts.
# 5.Save the scene map output from FAST-LIO
# 6. Collect reference data: put robot back start point, run FAST_LIO_LOCALIZATION and path follow code
python3 UMAD/robot_system_code/script/path_follow.py
rosrun fast_lio_localization publish_initial_pose.py 0 0 0 0 0 0
roslaunch fast_lio_localization localization_mid360.launch map:=/home/imrl/Desktop/3.Central-Avenue.pcd
rosbag record /camera/color/image_raw/compressed /localization
# Assuming a long time has passed, or you have placed some anomalous Objects in the scene.
# 7.Collect query data: put robot back start point, run FAST_LIO_LOCALIZATION and path follow code like 6
python3 UMAD/robot_system_code/script/path_follow.py
rosrun fast_lio_localization publish_initial_pose.py 0 0 0 0 0 0
roslaunch fast_lio_localization localization_mid360.launch map:=/home/imrl/Desktop/3.Central-Avenue.pcd
rosbag record /camera/color/image_raw/compressed /localization
```
## Acknowledgement
The authors would like to thank the following people for their contributions to data collection and data annotation for this project: [@Xiangyu QIN](https://github.com/carter-qin), [@Shenbo WANG](https://github.com/20191864135), [@Kaijie YIN](https://github.com/exaids66), [@Shuhao ZHAI](https://github.com/LynnZoe), [@Xiaonan LI](https://github.com/12mango), [@Beibei ZHOU](https://github.com/zbb9999), and [@Hongzhi CHENG](https://github.com/chenghz).
## License
Our datasets and code is released under the MIT License (see LICENSE file for details).
## Citing
If you find our work useful, please consider citing:
```
@inproceedings{li2024umad,
title={UMAD: University of Macau Anomaly Detection Benchmark Dataset},
author={Li, Dong and Chen, Lineng and Xu, Cheng-Zhong and Kong, Hui},
booktitle={2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={5836--5843},
year={2024},
organization={IEEE}
}
```
## Note
You can contact Dong Li via email([email protected]) or [open an issue on UMAD repo](https://github.com/IMRL/UMAD/issues) directly If you have any questions.