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https://github.com/wh200720041/iscloam

Intensity Scan Context based full SLAM implementation for autonomous driving. ICRA 2020
https://github.com/wh200720041/iscloam

aloam autonomous-driving loam localization slam

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Intensity Scan Context based full SLAM implementation for autonomous driving. ICRA 2020

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README 

        
# ISCLOAM 

## Intensity Scan Context based Full SLAM Implementation (ISC-LOAM) 



This work is an implementation of paper "Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection" in IEEE International Conference on Robotics and Automation 2020 (ICRA) [paper](https://arxiv.org/pdf/2003.05656.pdf)

This work is 3D lidar based Simultaneous Localization And Mapping (SLAM), including both front-end and back-end SLAM, at 20Hz. 



**Author:** [Wang Han](http://wanghan.pro), Nanyang Technological University, Singapore



For front-end only odometry, you may visit [FLOAM (fast lidar odometry and mapping)](https://github.com/wh200720041/floam)

## 1. Evaluation

### 1.1. Demo

Watch our demo at [Video Link](https://youtu.be/Kfi6CFK4Ke4)












### 1.2. Mapping Example












### 1.3. Localization Example








### 1.4. Ground Truth Comparison

Green: ISCLOAM        Red: Ground Truth










                      KITTI sequence 00                                  KITTI sequence 05



### 1.5. Localization error  



Platform: Intel® Core™ i7-8700 CPU @ 3.20GHz 



Average translation error : 1.08%



Average rotation error : 0.000073



### 1.6. Comparison

| Dataset                                      | ISCLOAM                    | FLOAM                  |

|----------------------------------------------|----------------------------|------------------------|

| `KITTI sequence 00`                          | 0.24%                      | 0.51%                  |

| `KITTI sequence 05`                          | 0.22%                      | 0.93%                  |



## 2. Prerequisites

### 2.1 **Ubuntu** and **ROS**

Ubuntu 64-bit 20.04.



ROS Noetic. [ROS Installation](http://wiki.ros.org/ROS/Installation)



### 2.2. **Ceres Solver**

Follow [Ceres Installation](http://ceres-solver.org/installation.html).



### 2.3. **PCL**

Follow [PCL Installation](http://www.pointclouds.org/downloads/linux.html).



### 2.3. **GTSAM**

Follow [GTSAM Installation](https://gtsam.org/get_started/).



### 2.3. **OPENCV**

Follow [OPENCV Installation](https://opencv.org/releases/).



### 2.4. **Trajectory visualization**

For visualization purpose, this package uses hector trajectory sever, you may install the package by 

```

sudo apt-get install ros-noetic-hector-trajectory-server

```

Alternatively, you may remove the hector trajectory server node if trajectory visualization is not needed



## 3. Build 

### 3.1 Clone repository:

```

cd ~/catkin_ws/src

git clone https://github.com/wh200720041/iscloam.git

cd ..

catkin_make -j1

source ~/catkin_ws/devel/setup.bash

```



### 3.2 Download test rosbag

Download [KITTI sequence 05](https://drive.google.com/file/d/1eyO0Io3lX2z-yYsfGHawMKZa5Z0uYJ0W/view?usp=sharing) (10GB) or [KITTI sequence 07](https://drive.google.com/file/d/1_qUfwUw88rEKitUpt1kjswv7Cv4GPs0b/view?usp=sharing) (4GB)



Unzip compressed file 2011_09_30_0018.zip. If your system does not have unzip. please install unzip by 

```

sudo apt-get install unzip 

```



This may take a few minutes to unzip the file, by default the file location should be /home/user/Downloads/2011_09_30_0018.bag

```

cd ~/Downloads

unzip ~/Downloads/2011_09_30_0018.zip

```



### 3.3 Launch ROS

```

roslaunch iscloam iscloam.launch

```



### 3.4 Mapping Node

if you would like to generate the map of environment at the same time, you can run 

```

roslaunch iscloam iscloam_mapping.launch

```

Note that the global map can be very large, so it may takes a while to perform global optimization, some lag is expected between trajectory and map since they are running in separate thread. More CPU usage will happen when loop closure is identified.



## 4. Test other sequence

To generate rosbag file of kitti dataset, you may use the tools provided by 

[kitti_to_rosbag](https://github.com/ethz-asl/kitti_to_rosbag) or [kitti2bag](https://github.com/tomas789/kitti2bag) 



## 5. Other Velodyne sensor

You may use iscloam_velodyne.launch for your own velodyne sensor, such as Velodyne VLP-16. 



## 6. Citation

If you use this work for your research, you may want to cite the paper below, your citation will be appreciated 

```

@inproceedings{wang2020intensity,

  author={H. {Wang} and C. {Wang} and L. {Xie}},

  booktitle={2020 IEEE International Conference on Robotics and Automation (ICRA)}, 

  title={Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection}, 

  year={2020},

  volume={},

  number={},

  pages={2095-2101},

  doi={10.1109/ICRA40945.2020.9196764}

}

```



## 7.Acknowledgements

Thanks for [A-LOAM](https://github.com/HKUST-Aerial-Robotics/A-LOAM) and LOAM(J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time) and [LOAM_NOTED](https://github.com/cuitaixiang/LOAM_NOTED).