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https://github.com/gisbi-kim/SC-LeGO-LOAM

backup of irapkaist/SC-LeGO-LOAM
https://github.com/gisbi-kim/SC-LeGO-LOAM

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# 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  Scan Context C++ , the LiDAR place recognition method, for LiDAR SLAM applications.  

- For more details for each algorithm please refer to 


  Scan Context https://github.com/irapkaist/scancontext 


  LeGO LOAM https://github.com/facontidavide/LeGO-LOAM-BOR 


- 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  nanoflann. 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

-  Video 1: DCC (MulRan dataset)

-  Video 2: Riverside (MulRan dataset) 

-  Video 3: KAIST (MulRan dataset) 








## 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  here (py-icp slam) . 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  MulRan dataset  and use the  ROS topic publishing tool .   



## 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 (`[email protected]`)



## 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!