# SC-LeGO-LOAM

## What is SC-LeGO-LOAM?

- SC-LeGO-LOAM integrated LeGO-LOAM for lidar odometry and 2 different loop closure methods: ScanContext and Radius search based loop closure. While ScanContext is correcting large drifts, radius search based method is good for fine-stitching.

## Repository Information

### Original Repository link

[https://github.com/gisbi-kim/SC-LeGO-LOAM](<https://github.com/gisbi-kim/SC-LeGO-LOAM>)

### Required Sensors

- LIDAR \[VLP-16, HDL-32E, VLS-128, Ouster OS1-16, Ouster OS1-64\]
- IMU \[9-AXIS\]

### ROS Compatibility

- ROS 1

### Dependencies

- ROS
- PCL
- GTSAM

```bash
wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.0-alpha2.zip
cd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/
cd ~/Downloads/gtsam-4.0.0-alpha2/
mkdir build && cd build
cmake ..
sudo make install
```

## Build &amp; Run

### 1\) Build

```bash
cd ~/catkin_ws/src
git clone https://github.com/gisbi-kim/SC-LeGO-LOAM.git
cd ..
catkin_make
```

### 2\) Set parameters

- Set imu and lidar topic on `include/utility.h`
- Set lidar properties on `include/utility.h`
- Set scan context settings on `include/Scancontext.h`

(Do not forget to rebuild after setting parameters.)

### 3\) Run

```bash
source devel/setup.bash
roslaunch lego_loam run.launch
```

## Example Result

## Other Examples

- [Video 1: DCC (MulRan dataset)](<https://youtu.be/MtQ8-PiBK3E?t=194>)
- [Video 2: Riverside (MulRan dataset)](<https://youtu.be/p-NsVs8GATA?t=436>)
- [Video 3: KAIST (MulRan dataset)](<https://youtu.be/bEqCehMJ_Hk>)

## MulRan dataset

- If you want to reproduce the results as the above video, you can download the [MulRan dataset](<https://sites.google.com/view/mulran-pr/home>) and use the [ROS topic publishing tool](<https://sites.google.com/view/mulran-pr/tool>) .

## Cite SC-LeGO-LOAM

```bash
@INPROCEEDINGS { gkim-2018-iros,
  author = {Kim, Giseop and Kim, Ayoung},
  title = { Scan Context: Egocentric Spatial Descriptor for Place Recognition within {3D} Point Cloud Map },
  booktitle = { Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems },
  year = { 2018 },
  month = { Oct. },
  address = { Madrid }
}
```

and

```bibtex
@inproceedings{legoloam2018,
  title={LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain},
  author={Shan, Tixiao and Englot, Brendan},
  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={4758-4765},
  year={2018},
  organization={IEEE}
}
```

## Contact

- Maintainer: Giseop Kim (`paulgkim@kaist.ac.kr`)