floam_g2o

This repository is a modified LiDAR-inertial odometry system, which is developed based on the open-source odometry framework FLOAM.

Modification

• Use g2o instead of ceres

Compare with original floam

demo

Test with kitti data

-Bilibili video

Install

Use the following commands to download and compile the package.

cd ~/catkin_ws/src
git clone https://github.com/chengwei0427/floam_g2o.git
cd ..
catkin_make 

Other notes

1. you should change the cmakelist, find the right dependencies; I think you can finish the work yourself

2.you could (W/O)comment the #define USE_G2O in odomEstimationClass.h to G2o or Ceres(original) method;

3. you could modify the params in floam_mapping.launch to set whether to save the gt-traj or laserodom-traj.

TODO

• [add right perturbation]

--------------=---------------------------- divide line ----------------------------------------------

FLOAM

Fast LOAM (Lidar Odometry And Mapping)

This work is an optimized version of A-LOAM and LOAM with the computational cost reduced by up to 3 times. This code is modified from LOAM and A-LOAM .

Modifier: Wang Han, Nanyang Technological University, Singapore

1. Demo Highlights

Watch our demo at Video Link

2. Evaluation

2.1. Computational efficiency evaluation

Computational efficiency evaluation (based on KITTI dataset): Platform: Intel® Core™ i7-8700 CPU @ 3.20GHz

Dataset	ALOAM	FLOAM
KITTI	151ms	59ms

Localization error:

Dataset	ALOAM	FLOAM
KITTI sequence 00	0.55%	0.51%
KITTI sequence 02	3.93%	1.25%
KITTI sequence 05	1.28%	0.93%

2.2. localization result

2.3. mapping result

3. Prerequisites

3.1 Ubuntu and ROS

Ubuntu 64-bit 18.04.

ROS Melodic. ROS Installation

3.2. Ceres Solver

Follow Ceres Installation.

3.3. PCL

Follow PCL Installation.

3.4. Trajectory visualization

For visualization purpose, this package uses hector trajectory sever, you may install the package by

sudo apt-get install ros-melodic-hector-trajectory-server

Alternatively, you may remove the hector trajectory server node if trajectory visualization is not needed

4. Build

4.1 Clone repository:

    cd ~/catkin_ws/src
    git clone https://github.com/wh200720041/floam.git
    cd ..
    catkin_make
    source ~/catkin_ws/devel/setup.bash

4.2 Download test rosbag

Download KITTI sequence 05 or KITTI sequence 07

Unzip compressed file 2011_09_30_0018.zip. If your system does not have unzip. please install unzip by

sudo apt-get install unzip 

And this may take a few minutes to unzip the file

	cd ~/Downloads
	unzip ~/Downloads/2011_09_30_0018.zip

4.3 Launch ROS

    roslaunch floam floam.launch

if you would like to create the map at the same time, you can run (more cpu cost)

    roslaunch floam floam_mapping.launch

If the mapping process is slow, you may wish to change the rosbag speed by replacing "--clock -r 0.5" with "--clock -r 0.2" in your launch file, or you can change the map publish frequency manually (default is 10 Hz)

5. Test on other sequence

To generate rosbag file of kitti dataset, you may use the tools provided by kitti_to_rosbag or kitti2bag

6. Test on Velodyne VLP-16 or HDL-32

You may wish to test FLOAM on your own platform and sensor such as VLP-16 You can install the velodyne sensor driver by

sudo apt-get install ros-melodic-velodyne-pointcloud

launch floam for your own velodyne sensor

    roslaunch floam floam_velodyne.launch

If you are using HDL-32 or other sensor, please change the scan_line in the launch file

7.Acknowledgements

Thanks for A-LOAM and LOAM(J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time) and LOAM_NOTED.

8. Citation

If you use this work for your research, you may want to cite

@inproceedings{wang2021,
  author={H. {Wang} and C. {Wang} and C. {Chen} and L. {Xie}},
  booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
  title={F-LOAM : Fast LiDAR Odometry and Mapping}, 
  year={2020},
  volume={},
  number={}
}