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https://github.com/url-kaist/Quatro-LeGO-LOAM

Last synced: 4 months ago
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Host: GitHub
URL: https://github.com/url-kaist/Quatro-LeGO-LOAM
Owner: url-kaist
License: gpl-3.0
Created: 2022-03-28T17:59:21.000Z (about 2 years ago)
Default Branch: main
Last Pushed: 2022-09-02T07:11:41.000Z (almost 2 years ago)
Last Synced: 2024-01-16T05:33:39.063Z (5 months ago)
Language: C++
Size: 64.9 MB
Stars: 57
Watchers: 2
Forks: 6
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: License

Lists

Awesome-Autonomous-Driving - Quatro-LeGO-LOAM - LOAM (5 Localization and SLAM / 3.4 High Performance Inference)

README

# Quatro-LeGO-LOAM
## Robust Global Registration Quatro (22' ICRA) + LeGO-LOAM (18' IROS)
- For more details for each algorithm,

Quatro https://github.com/url-kaist/quatro

LeGO LOAM https://github.com/RobustFieldAutonomyLab/LeGO-LOAM

- In [Quatro](https://github.com/url-kaist/quatro) registration example, we used fast point feature histogram (FPFH), Patchwork, and etc. to reduce the computational time of feature extraction & matching, i.e. the front-end of global registration, from tens of seconds to almost 0.2 sec.
- *But note that Quatro-LeGO-LOAM only uses the FPFH descriptor to perform feature matching and then estimates a relative pose through Quatro.*
- **LeGO-LOAM has a large drift, which is accumulated in large-scale maps. For this reason, loop detection using Radius-Search may not generate the loop closure constraint properly. Therefore, if the drift is larger than the used KITTI 05 sequence, it is better to use another descriptor or another Odometry method. Then, You can use ~~[Quatro-SC-LeGO-LOAM]() or [Quatro-Faster-LIO]()~~ alterlatively.** It hasn't been released yet.😭

## Test Env.

The code is tested successfully at
* Ubuntu 18.04 LTS + ROS Melodic [main branch](https://github.com/kimdaebeom/Quatro-LeGO-LOAM/tree/main)
* Ubuntu 20.04 LTS + ROS Noetic [noetic branch](https://github.com/kimdaebeom/Quatro-LeGO-LOAM/tree/noetic)

## Build Quatro

- First of all, you need to build [Quatro (link)](https://github.com/url-kaist/quatro).
* Run the following script. We use [catkin tools](https://catkin-tools.readthedocs.io/en/latest/),

```
mkdir -p ~/catkin_ws/src
cd ~/catkin_ws/src
git clone [email protected]:url-kaist/quatro.git
cd quatro && mkdir build && cd build
# To build Quatro, `pmc-src` should be placed in `build` directory in catkin workspace
# i.e. `~/catkin_ws/build/pmc-src`
cmake ..
mv pmc-src/ ../../../build/
cd ~/catkin_ws
catkin build quatro
```

**Note that without `pmc-src`**, the below error occurs!

```
CMake Error at quatro/CMakeLists.txt:53 (add_subdirectory):
add_subdirectory given source "~/catkin_ws/build/pmc-src" which
is not an existing directory.
```

## How to run
```
cd ~/catkin_ws/src
git clone {this repo}
cd ..
catkin_make
source devel/setup.bash
roslaunch lego_loam run.launch
```
## Dataset
### KITTI 05 sequence dataset
- Download KITTI 05 sequence dataset with following command. We already make a rosbag file as an example *(15.6GB)*
```
wget https://urserver.kaist.ac.kr/publicdata/quatro/kitti_sequence_05.bag
```

- You can run the KITTI bag file as follows:
```
rosbag play kitti_sequence_05.bag --clock --topics /kitti/velo/pointcloud
```

### Else
- To generate more bags using other [KITTI raw data](http://www.cvlibs.net/datasets/kitti/eval_odometry.php), you can use the python script [kitti2bag](https://github.com/tomas789/kitti2bag).
- Check the parameters according to the various lidars in *utility.h* : `VLP-16` `HDL-32E` `HDL-64E` `VLS-128` `OS1-16` `OS1-64` `OS0-128`
- You can run your bag file as below code.
```
rosbag play {your_bag}.bag --topics {pointcloud_topic_message}
```

## Trajectory Evaluation
- I used [evo (link)](https://github.com/MichaelGrupp/evo) to evaluate the trajectories.
- You can install evo using the following command

```
pip install evo --upgrade --no-binary evo
```

- First, you have to modify the directory `traDirectory` in `utility.h` file.
```cpp
// at 62 line of utility.h
extern const string traDirectory = "/home/{your_dir}/lego_loam_trajectory/";
```

- The following command can be used to evaluate the trajectory.
```
evo_traj kitti {your_file_name}.txt --ref={kitti_groundtruth_file_name}.txt -p --plot_mode=xyz
```

- Also, You can use other methods to evaluate your trajectories e.g. metric of trajectory.
```
mkdir results
evo_ape kitti [kitti_file_name].txt [your_file_name].txt -va --plot --plot_mode xyz --save_results results/[name].zip
evo_ape kitti [kitti_file_name].txt [your_file_name].txt -va --plot --plot_mode xyz --save_results results/[name]
```

- You can also save final PCD file. Modify your `pcdDirectory` in `utility.h` file.
```cpp
// at 60 line of utility.h
extern cost string pcdDirectory = "/home/{your_dir}"
```

## Example
- Compare LeGO-LOAM and Quatro-LeGO-LOAM with KITTI 05 dataset. We used **cloudcompare** to visualize pcl.
- You can download cloudcompare with folloing command.

```
snap install cloudcompare
```

- Trajectory is evaluated by [evo (link)](https://github.com/MichaelGrupp/evo).

### Compare PointCloud

- Compare by `CloudCompare`.
![PointCloud](results/kitti_compare.gif?w=550)

- From the gif below, `red : source`, `green : target`, `blue : estimation`.
![video](results/compare_vid.gif?w=550)

- Even though they are the same Radius Search and ICP parameters, they differ greatly depending on **Quatro's Initial Guess**.
- Existing Lego-loam does not satisfy the icp threshold due to the distance between src and tgt, but using ICP after Quatro produces much better ICP results.

### Compare Trajectory with GroundTruth
- Quatro-Lego-Loam's Trajectory evaluation showed an error of less than **10cm** !!

## Visualize Quatro Process
- You can visualize Quatro's *source, target, estimation* clouds : `/quatro_src`, `/quatro_tgt`, `/quatro_est`.
- Below visualization picture is an example. `red : source`, `green : target`, `blue : estimation`