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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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backup of irapkaist/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!