PL-VIO-Learning

说明

对贺博的PL-VIO中的线特征操作的几个文件夹的注释

本次是对贺一佳博士的PL-VIO进行学习并注释.十分感谢贺博之前做的工作. PL-VIO是在VINS-Mono的基础上,添加了线特征,并且取得了不错的效果.缺点是线特征的提取匹配所需要的时间比较长.

github: https://github.com/HeYijia/PL-VIO

论文:PL-VIO: Tightly-Coupled Monocular Visual-Inertial Odometry Using Point and Line Features.

同时,还少量借鉴了VINS-Mono的注释版: https://github.com/kuankuan-yue/VINS-Mono-Learning

主要注释了其中的三个文件,其作用是对图像进行线特征的提取,匹配和发布.

linefeature_tracker.cpp
linefeature_tracker.h
linefeature_tracker_node.cpp

以下是PL-VIO的原README

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PL-VIO

Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features

Compared with point features, lines provide significantly more geometry structure information of the environment. We proposed PL-VIO a tightly-coupled monocular visual-inertial odometry system exploiting both point and line features. This code runs on Linux, and is fully integrated with ROS.

1. Prerequisites

1.1 Ubuntu and ROS Ubuntu 16.04. ROS Kinetic, ROS Installation additional ROS pacakge

	sudo apt-get install ros-YOUR_DISTRO-cv-bridge ros-YOUR_DISTRO-tf ros-YOUR_DISTRO-message-filters ros-YOUR_DISTRO-image-transport

If you install ROS Kinetic, please update opencv3 with

    sudo apt-get install ros-kinetic-opencv3

1.2. Ceres Solver Follow Ceres Installation, remember to make install.

2. Build PL-VIO on ROS

Clone the repository and catkin_make:

    cd ~/catkin_ws/src
    git clone https://github.com/HeYijia/PL-VIO.git
    cd ../
    catkin_make
    source ~/catkin_ws/devel/setup.bash

3. Run with Simulation Dataset

we have provide the simulation data, you can test plvio with it.

	roslaunch plvio_estimator simdata_fix.launch 
	roslaunch plvio_estimator vins_rviz.launch 

green for trajectory, blue for point and line landmarks.

Notice: if you want to generate your own data, you can refer to the code below.

	https://github.com/HeYijia/vio_data_simulation

4.Performance on EuRoC dataset

4.1 Run with feature.bag

Since line detection and matching are time consuming, we can record the feature results from the front-end module as feature.bag and test PL-VIO's back-end module. You can find mh05_feature.bag in config fold and test our system with it:

	roslaunch plvio_estimator euroc_fix_offline.launch 
	roslaunch plvio_estimator vins_rviz.launch 
	rosbag play -r 5 config/mh05_feature.bag 

4.2 Run with EuRoC dataset directly

4.2.1 Download EuRoC MAV Dataset. Although it contains stereo cameras, we only use one camera.

4.2.2 Open three terminals, launch the vins_estimator , rviz and play the bag file respectively. Take MH_05 as example

    roslaunch plvio_estimator euroc_fix_extrinsic.launch 
    roslaunch plvio_estimator vins_rviz.launch 
    rosbag play -r 0.2 YOUR_PATH_TO_DATASET/MH_05_difficult.bag 

(If you fail to open vins_rviz.launch, just open an empty rviz, then load the config file: file -> Open Config-> YOUR_VINS_FOLDER/config/vins_rviz_config.rviz) **Notice: ** Please play your bag with 0.2x speed since the time consuming from line detection.

5 Related Papers

• PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features, Yijia He, Ji Zhao, Yue Guo, Wenhao He and Kui Yuan.

@article{he2018pl,
  title={Pl-vio: Tightly-coupled monocular visual--inertial odometry using point and line features},
  author={He, Yijia and Zhao, Ji and Guo, Yue and He, Wenhao and Yuan, Kui},
  journal={Sensors},
  volume={18},
  number={4},
  pages={1159},
  year={2018},
  publisher={Multidisciplinary Digital Publishing Institute}
}

If you use PL-VIO for your academic research, please cite our related papers.

6. Acknowledgements

We use VINS-Mono as our base line code. Thanks Dr. Qin Tong, Prof. Shen etc very much.

7. Licence

The source code is released under GPLv3 license.

We are still working on improving the code reliability. For any technical issues, please contact Yijia He heyijia_2013@163.com.