LVI_SAM_fixed

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(Ubuntu 20.04 + noetic)LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping

LVI_SAM_fixed 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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# LVI-SAM-fixed

Run on ubuntu 20.04 + noetic

## 1. ThirdParties

- gtsam : 4.1rc

- Eigen : 3.3.7

- gflags : 2.2.2

- Ceres : 1.14.0

- Boost : 1.71.0

## 2. Install ros pkgs

```shell
sudo apt-get install -y ros-noetic-navigation ros-noetic-robot-localization ros-noetic-robot-state-publisher
```

# LVI-SAM

This repository contains code for a lidar-visual-inertial odometry and mapping system, which combines the advantages of [LIO-SAM](https://github.com/TixiaoShan/LIO-SAM/tree/a246c960e3fca52b989abf888c8cf1fae25b7c25) and [Vins-Mono](https://github.com/HKUST-Aerial-Robotics/VINS-Mono) at a system level.

---

## Dependency

- [ROS](http://wiki.ros.org/ROS/Installation) (Tested with kinetic and melodic)
- [gtsam](https://github.com/borglab/gtsam/releases) (Georgia Tech Smoothing and Mapping library)
  ```
  wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.2.zip
  cd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/
  cd ~/Downloads/gtsam-4.0.2/
  mkdir build && cd build
  cmake -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF ..
  sudo make install -j4
  ```
- [Ceres](https://github.com/ceres-solver/ceres-solver/releases) (C++ library for modeling and solving large, complicated optimization problems)
  ```
  sudo apt-get install -y libgoogle-glog-dev
  sudo apt-get install -y libatlas-base-dev
  wget -O ~/Downloads/ceres.zip https://github.com/ceres-solver/ceres-solver/archive/1.14.0.zip
  cd ~/Downloads/ && unzip ceres.zip -d ~/Downloads/
  cd ~/Downloads/ceres-solver-1.14.0
  mkdir ceres-bin && cd ceres-bin
  cmake ..
  sudo make install -j4
  ```

---

## Compile

You can use the following commands to download and compile the package.

```
cd ~/catkin_ws/src
git clone https://github.com/TixiaoShan/LVI-SAM.git
cd ..
catkin_make
```

---

## Datasets

The datasets used in the paper can be downloaded from Google Drive. The data-gathering sensor suite includes: Velodyne VLP-16 lidar, FLIR BFS-U3-04S2M-CS camera, MicroStrain 3DM-GX5-25 IMU, and Reach RS+ GPS.

```
https://drive.google.com/drive/folders/1q2NZnsgNmezFemoxhHnrDnp1JV_bqrgV?usp=sharing
```

**Note** that the images in the provided bag files are in compressed format. So a decompression command is added at the last line of ```launch/module_sam.launch```. If your own bag records the raw image data, please comment this line out.

---

## Run the package

1. Configure parameters:

```
Configure sensor parameters in the .yaml files in the ```config``` folder.
```

2. Run the launch file:
```
roslaunch lvi_sam run.launch
```

3. Play existing bag files:
```
rosbag play handheld.bag 
```

## mp4 to gif

```shell
ffmpeg \
-i simplescreenrecorder-2021-04-29_23.21.12_MOTION.mp4 \
-r 30 \
-vf "scale=512:-1,split[s0][s1];[s0]palettegen[p];[s1][p]paletteuse" \
-ss 00:00:00 -to 00:01:00 \
LVI_SAM.gif
```

---

## Paper

Thank you for citing our [paper](./doc/paper.pdf) if you use any of this code or datasets.

```
@inproceedings{lvisam2021shan,
  title={LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping},
  author={Shan, Tixiao and Englot, Brendan and Ratti, Carlo and Rus Daniela},
  booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
  pages={to-be-added},
  year={2021},
  organization={IEEE}
}
```

---

## Acknowledgement

- The visual-inertial odometry module is adapted from [Vins-Mono](https://github.com/HKUST-Aerial-Robotics/VINS-Mono).
  - The lidar-inertial odometry module is adapted from [LIO-SAM](https://github.com/TixiaoShan/LIO-SAM/tree/a246c960e3fca52b989abf888c8cf1fae25b7c25).

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