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

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

Follow Ceres Installation.

Follow PCL Installation.

Follow OpenCV Installation.

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

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.

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 

• 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