VI-LOAM_ISLAB

Visual Inertial Lidar Odometry and Mapping

This repository contains code for a VI-LOAM system, which combines the advantages of A-LOAM and Vins-Mono at a system level.

A-LOAM is an Advanced implementation of LOAM (J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time), which uses Eigen and Ceres Solver to simplify code structure. This code is modified from LOAM and LOAM_NOTED. This code is clean and simple without complicated mathematical derivation and redundant operations.

Modifier: Didula Dissanayaka

1. Prerequisites

1.1 Ubuntu and ROS

Ubuntu 64-bit 16.04, 18.04 or 20.04. ROS Kinetic, Melodic or. ROS Installation

1.2. Ceres Solver

Follow Ceres Installation.

1.3. PCL

Follow PCL Installation.

1.4. OpenCV 3.4 or higher

Follow OpenCV Installation.

2. Build VI-LOAM

Clone the repository and catkin_make:

    cd ~/catkin_ws/src
    git clone https://github.com/didzdissanayaka8/VI-LOAM_ISLAB.git
    cd ../
    catkin_make
    source ~/catkin_ws/devel/setup.bash

3. Datasets

LVI-SAM Dataset

Download LVI-SAM Dataset. The dataset include following sensors: Velodyne VLP-16 lidar, FLIR BFS-U3-04S2M-CS camera, MicroStrain 3DM-GX5-25 IMU, and Reach RS+ GPS.

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.

AI4L Dataset

4. Run the package

1. Configure parameters:

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

2. Run the launch file:

roslaunch viloam run.launch

3. Play existing bag files:

rosbag play handheld.bag 

5.Acknowledgements

• The visual-inertial odometry module is adapted from Vins-Mono.
• The lidar-inertial odometry module is adapted from LIO-SAM.
• Tightly-coupled lidar-visual inertial odometry and mapping LVI-SAM