# D-LIOM

**Repository Path**: latristan/D-LIOM

## Basic Information

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

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2023-11-19
- **Last Updated**: 2023-12-12

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## D-LIOM: Tightly-coupled Direct LiDAR-Inertial Odometry and Mapping

> **Our paper**: we have corrected some typos and errors in Section III-D of the previous version of the paper, the revised version can be accessed [here](https://github.com/peterWon/D-LIOM/blob/master/paper/TMM_D_LIOM_FINAL_VERSION.pdf). When amending our paper, I would like to say thanks to [Sky Shaw](https://www.zhihu.com/people/sky-shaw-jhu), who has found my errors and warmly provided his suggestions.
>
> **Related video:** A running demo can be found at https://youtu.be/21J2QLUQbno or at https://www.bilibili.com/video/BV14y4y157du.
---
### Sensor suite supported
- 6-axis IMU + Velodyne/Ouster/Robosense LiDAR (Livox LiDAR can be also supported if the data of which is tansformed to the format of velodyne's, i.e., PointXYZRT.)
---
### Prerequisites
- Install depends follow the instructions of ["cartographer"](https://google-cartographer.readthedocs.io/en/latest/index.html) and ["cartographer_ros"](https://google-cartographer-ros.readthedocs.io/en/latest/compilation.html) respectively. 
- Build [OpenCV 3.3.1](https://opencv.org/releases/page/2/) (other version may work fine if it includes implementation of SURF keypoint detector) from source with "opencv_contrib"
- Build [GTSAM 4.0.2](https://github.com/borglab/gtsam/tree/4.0.2) with compile options as follows:
  
  ```cmake -DGTSAM_USE_SYSTEM_EIGEN=ON -DGTSAM_WITH_TBB=OFF -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF -DGTSAM_COMPILE_OPTIONS_PUBLIC="" ..```
- Build [PCL 1.8.0]() with compile options as follows:
  
  ```cmake -DPCL_ENABLE_SSE=OFF ..```
---
### Build
- Clone this repository in your ROS workspace

- Set CMake variable "OpenCV_DIR" in the root [CMakeLists.txt](src/cartographer/CMakeLists.txt) of "cartographer". 

- Return to the root folder of your ROS workspace (which has the same directory as this "README" file) and run:

      catkin_make_isolated
---
### Run an online demo
#### TONGJI Dataset
- Some demo sequences can be downloaded via: 

  - TONGJI dataset: [link](https://pan.baidu.com/s/12Y6CErygofwPf9f8FrNLqA), pwd: hfrl

- ```roslaunch dlio demo_dlio_tongji.launch bag_filename:=BAG_FILE_PATH```
  where BAG_FILE_PATH is the full path of the downloaded bag file in your device

- Some results on the self-collected TONGJI dataset.

  <div align="center">
      <img src="img/rot.png">  
  </div>

  <div align="center">
      <img src="img/hc.png">  
  </div>
#### NTU-VIRAL

- The dataset is available online at [NTU-VIRAL](https://ntu-aris.github.io/ntu_viral_dataset/)

-     ```roslaunch dlio demo_dlio_viral.launch bag_filename:=BAG_FILE_PATH```
  where BAG_FILE_PATH is the full path of the downloaded bag file in your device
  
-     Results on VIRAL
  
  <div align="center">
      <img src="img/ntu_eee.png">  
  </div>
  
  <div align="center">
      <img src="img/ntu_nya.png">  
  </div>
  
  <div align="center">
      <img src="img/ntu_sbs.png">  
  </div>
  
  
  
  
#### KAIST-Complex Urban Dataset
- The dataset is available online at [KAIST](https://irap.kaist.ac.kr/dataset/)

- Play KAIST bag follow the instruction: [file_player](https://irap.kaist.ac.kr/dataset/development.html)

- ```roslaunch dlio demo_dlio_kaist.launch```

- Results on KAIST "Urban-09" and "Urban-10"

  <div align="center">
      <img src="img/kaist.png">  
  </div>
#### Your own data
- Write a urdf file of your device and save it to folder ['urdf'](src/dlio/urdf)
- Write a configuration file of your data and save it to ['config'](src/dlio/config)
- Write a launch file of your data and save it to ['launch'](src/dlio/launch)
- Run your bag as above examples
---
### Run offline and view mapping result
#### Launch dlio offline
    roslaunch dlio dlio_offline_tongji bag_filenames:=BAG_FILE_PATH
#### View mapping result
    roslaunch dlio dlio_map_viewer_rectified urdf_filename:=tongji pose_graph_pb_filename:=PG_FILENAME range_data_pb_filename:=RD_FILENAME rate:=100
  where PG_FILENAME and RD_FILENAME are binary pbstream files saved to disk when launch the offline script above.

---
### Acknowledgements
- The authors of [cartographer](https://github.com/cartographer-project/cartographer)
- The authors of [cartographer_ros](https://github.com/cartographer-project/cartographer_ros)
- The authors of [LIO-SAM](https://github.com/TixiaoShan/LIO-SAM)
- The authors of [VINS-Mono](https://github.com/HKUST-Aerial-Robotics/VINS-Mono)
- The authors of [VIO-Doc](https://github.com/StevenCui/VIO-Doc)