# SC-LeGO-LOAM

**Repository Path**: caomengnb/SC-LeGO-LOAM

## Basic Information

- **Project Name**: SC-LeGO-LOAM
- **Description**: SC-LeGO-LOAM backup
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-10-17
- **Last Updated**: 2022-10-17

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# SC-LeGO-LOAM
## NEWS (Nov, 2020)
- A Scan Context integration for LIO-SAM, named [SC-LIO-SAM (link)](https://github.com/gisbi-kim/SC-LIO-SAM), is also released. 

## Real-time LiDAR SLAM: Scan Context (18 IROS) + LeGO-LOAM (18 IROS)
- This repository is an example use-case of <a href="https://github.com/irapkaist/scancontext/tree/master/cpp"> Scan Context C++ </a>, the LiDAR place recognition method, for LiDAR SLAM applications.  
- For more details for each algorithm please refer to <br>
  Scan Context https://github.com/irapkaist/scancontext <br>
  LeGO LOAM https://github.com/facontidavide/LeGO-LOAM-BOR <br>
- Just include `Scancontext.h`. For details see the file `mapOptmization.cpp`. 
- This example is integrated with LOAM, but our simple module (i.e., `Scancontext.h`) can be easily integrated with any other key-frame-based odometry (e.g., wheel odometry or ICP-based odometry).
- Current version: April, 2020.  


## Features 
- Light-weight: a single header and cpp file named "Scancontext.h" and "Scancontext.cpp"
    - Our module has KDtree and we used <a href="https://github.com/jlblancoc/nanoflann"> nanoflann</a>. nanoflann is an also single-header-program and that file is in our directory.
- Easy to use: A user just remembers and uses only two API functions; `makeAndSaveScancontextAndKeys` and `detectLoopClosureID`.
- Fast: The loop detector runs at 10-15Hz (for 20 x 60 size, 10 candidates)


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


<p align="center"><img src="results/mulran_merged.png" width=900></p>
<p align="center"><img src="results/pangyo_merged.png" width=900></p>


## Scan Context integration

- For implementation details, see the `mapOptmization.cpp`; all other files are same as the original LeGO-LOAM.
- Some detail comments
    - We use non-conservative threshold for Scan Context's nearest distance, so expect to maximise true-positive loop factors, while the number of false-positive increases.
    - To prevent the wrong map correction, we used Cauchy (but DCS can be used) kernel for loop factor. See `mapOptmization.cpp` for details. (the original LeGO-LOAM used non-robust kernel). We found that Cauchy is emprically enough.
    - We use both two-type of loop factor additions (i.e., radius search (RS)-based as already implemented in the original LeGO-LOAM and Scan context (SC)-based global revisit detection). See `mapOptmization.cpp` for details. SC is good for correcting large drifts and RS is good for fine-stitching.
    - Originally, Scan Context supports reverse-loop closure (i.e., revisit a place in a reversed direction) and examples in <a href="https://github.com/kissb2/PyICP-SLAM"> here (py-icp slam) </a>. Our Scancontext.cpp module contains this feature. However, we did not use this for closing a loop in this repository because we found PCL's ICP with non-eye initial is brittle. 

## How to use 
- Place the directory `SC-LeGO-LOAM` under user catkin work space 
- For example, 
    ```
    cd ~/catkin_ws/src
    git clone https://github.com/irapkaist/SC-LeGO-LOAM.git
    cd ..
    catkin_make
    source devel/setup.bash
    roslaunch lego_loam run.launch
    ```

## MulRan dataset 
- If you want to reproduce the results as the above video, you can download the <a href="https://sites.google.com/view/mulran-pr/home"> MulRan dataset </a> and use the <a href="https://sites.google.com/view/mulran-pr/tool"> ROS topic publishing tool </a>.   


## Dependencies
- All dependencies are same as LeGO-LOAM (i.e., ROS, PCL, and GTSAM).
- We used C++14 to use std::make_unique in Scancontext.cpp but you can use C++11 with slightly modifying only that part.

## Cite SC-LeGO-LOAM
```
@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 
```
@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`)

## Misc notes
- You may also be interested in this (from the other author's) implementation :) 
  - ICRA20, ISCLOAM: Intensity Scan Context + LOAM, https://github.com/wh200720041/iscloam
  - Also light-weight and practical LiDAR SLAM codes!