Awesome

VINS-GPS-Wheel

Visual-Inertial Odometry Coupled with Wheel Encoder and GNSS

This repo couples wheel encoder data and GPS data on the basis of VINS_Mono. The project is tested on KAIST dataset and is suitable for automatic driving scenario.

The wheel encoder data is tightly coupled, referred to the paper[1]. GPS fusion adopts loose coupling, and the fusion method is consistent with VINS-Fusion.

Detailed derivations can be found in: https://blog.csdn.net/ewtewtewrt/article/details/117249295 The method has tested in KAIST dataset (urban28-pankyo) video

Install

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

1.2 Dependencies

Follow Ceres Installation, remember to make install. (Our testing environment: Ubuntu 16.04, ROS Kinetic, OpenCV 3.1.0, Eigen 3.3.7)

2. Build VINS-Mono on ROS

Clone the repository and catkin_make:

    cd ~/catkin_ws/src
    git clone https://github.com/Wallong/VINS-GPS-Wheel.git
    cd ..
    catkin_make
    source ~/catkin_ws/devel/setup.bash

3. Dataset

The method is tested on KAIST dataset. https://irap.kaist.ac.kr/dataset/

4. Example

Open four terminals, launch the vins_estimator, rviz and pubish the data file respectively. Take urban28-pankyo for example

    roslaunch vins_estimator kaist.launch 
    rosrun multisensor_fusion multisensor_fusion_node (optional, for GPS)
    rosrun vins_estimator kaist_pub YOUR_PATH_TO_DATASET/KAIST/urban28/urban28-pankyo
    roslaunch vins_estimator vins_rviz.launch

5. Plan

Module	Status
Encoder Pre-integration	Done
Initialization with encoder	Done
Optimization with encoder	Done
Online Extrinsic Calibration about encoder	Doing
Loosely coupled with GNSS	Done
Initialization with GNSS	Will do
Tightly coupled with GNSS	Will do

References

J. Liu, W. Gao and Z. Hu, "Visual-Inertial Odometry Tightly Coupled with Wheel Encoder Adopting Robust Initialization and Online Extrinsic Calibration," 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019, pp. 5391-5397, doi: 10.1109/IROS40897.2019.8967607.

Contact us

For any issues, please feel free to contact Longlong Wang: wanglonglong@tju.edu.cn