# FAST_LIO_LC

**Repository Path**: primer007/FAST_LIO_LC

## Basic Information

- **Project Name**: FAST_LIO_LC
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-03-28
- **Last Updated**: 2025-03-28

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# FAST_LIO_LC

The **tight** integration of [FAST-LIO](https://github.com/hku-mars/FAST_LIO) with Radius-Search-based loop closure module.

**FAST-LIO** (Fast LiDAR-Inertial Odometry) is a computationally efficient and robust LiDAR-inertial odometry package. It fuses LiDAR feature points with IMU data using a tightly-coupled iterated extended Kalman filter. But it doesn't have a loop closure module to eliminate the accumulated drift.

Therefore, this project implements the pose graph optimization with a radius-search-based loop closure module which refers to [FAST_LIO_SLAM](https://github.com/gisbi-kim/FAST_LIO_SLAM). And the pose and map in the iterated extended Kalman filter of FAST-LIO will be updated according to the optimization which is a key difference with [FAST_LIO_SLAM](https://github.com/gisbi-kim/FAST_LIO_SLAM).

- [FAST_LIO_LC](#fast_lio_lc)
  - [1. Prerequisites](#1-prerequisites)
  - [2. Build](#2-build)
  - [3. Quick test](#3-quick-test)
    - [3.1 For Velodyne 16](#31-for-velodyne-16)
  - [4. Example results](#4-example-results)
  - [Acknowledgements](#acknowledgements)

## 1. Prerequisites

- Ubuntu 18.04 and ROS Melodic
- PCL >= 1.8 (default for Ubuntu 18.04)
- Eigen >= 3.3.4 (default for Ubuntu 18.04)
- GTSAM >= 4.0.0(tested on 4.0.0-alpha2)

## 2. Build

```bash
cd YOUR_WORKSPACE/src
git clone https://github.com/yanliang-wang/FAST_LIO_LC.git
cd ..
catkin_make
```

## 3. Quick test

### 3.1 For Velodyne 16

You can test this project with [our data](https://drive.google.com/file/d/1NGTN3aULoTMp3raF75LwMu-OUtzUx-zX/view?usp=sharing) which includes `/velodyne_points`(10Hz) and `/imu/data`(400Hz).

```bash
roslaunch fast_lio mapping_velodyne.launch
roslaunch aloam_velodyne fastlio_velodyne_VLP_16.launch
rosbag play  T3F2-2021-08-02-15-00-12.bag  -r 2
```

> If you want to test the original FAST LIO (i.e. without the loop closure module), you can set `lc_enable` in the `mapping_velodyne.launch` to `false` and run following commands.
>
> ```bash
> roslaunch fast_lio mapping_velodyne.launch
> rosbag play  T3F2-2021-08-02-15-00-12.bag  -r 2
> ```

## 4. Example results

video: [Youtube link](https://youtu.be/W5HYYPYBrn8) , [Bilibili link](https://www.bilibili.com/video/BV1sT4y1D7DC/)

![example_results](doc/fast-lio-lc-output.gif)

## Acknowledgements 
In this project, the LIO module refers to [FAST-LIO](https://github.com/hku-mars/FAST_LIO) and the pose graph optimization refers to [FAST_LIO_SLAM](https://github.com/gisbi-kim/FAST_LIO_SLAM).

Many thanks for their work.