Point-Line Visual-Inertial Odometry with Optimized Line Feature

1. Prerequisites

1.1 Ubuntu and ROS

Ubuntu 20.04. ROS Noetic, please google it.

1.2. Dependency

Eigen 3.3.4 + OpenCV 4+ Cere-solver: Ceres Installation, remember to sudo make install.

1.3. Pretrained model

Download pretrained model the pretrained model and unzip the model to "hawp/src/outputs/hawp"

2. Build POL-VIO on ROS

Clone the repository and catkin_make (# note that you will create a new workspace named catkin_polvio):

mkdir -p ~/catkin_polvio/src    
cd ~/catkin_polvio/
catkin_make
source devel/setup.bash
echo $ROS_PACKAGE_PATH            
git clone https://github.com/HanqianSi/POL-VIO.git
catkin_make
source devel/setup.bash

3. Run on EuRoC dataset

Download EuRoC MAV Dataset.

run in the ~/catkin_polvio/

roslaunch polvio_estimator euroc_fix_extrinsic.launch

Now you should be able to run POL-VIO in the ROS RViZ.

3. Run on corridor dataset

Download corridor Dataset

run in the ~/catkin_polvio/

roslaunch polvio_estimator corridor_fix_extrinsic.launch

Note that: Different CPU and GPU maybe yield different results. Therefore, we suggest you test or compare methods on your machine by yourself.

4. Related Papers

Point-Line Visual-Inertial Odometry with Optimized Line Feature.

This paper is developed based on PL-VIO [1], VINS-Mono [2], PL-VINS[3] and EPLF-VINS[4].

[1] PL-VIO: Tightly-coupled monocular visual-inertial odometry using point and line features

[2] VINS-mono: A robust and versatile monocular visual-inertial state estimator

[3] PL-VINS: real-time monocular visual-inertial SLAM with point and line features

[4] EPLF-VINS: real-time monocular visual-inertial SLAM with efficient point-line flow features

If you find aforementioned works helpful for your research, please cite them.