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. Modification Highlights

This includes some optimization on the original implementation

Analytic methods is used instead of auto differentiation. This is performed on se3
Use linear motion prediction model to estimate the initial pose
Laser odometry and laser mapping are merged
A dynamic local map is used instead of global map, in order to save memory cost. Based on massive experiments, this only has slight influence on the performance.

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 then copy the file 2011_09_30_0018.bag into ~/catkin_ws/src/floam/dataset/ (this may take a few minutes to unzip the file)

	cd ~/catkin_ws/src/floam/dataset/
	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.

szm88/floam