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

ROS2 Migration: Yi-Chen Zhang, Isuzu Technical Center of America, USA

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 20.04.

ROS2 Foxy. ROS Installation

3.2. Ceres Solver

Follow Ceres Installation. Please checkout to 2.0.0 tag. ROS2 migrated version doest not support Ceres 2.1.0 yet.

3.3. PCL

For PCL library, please install by the following:

sudo apt-get install libpcl-dev ros-foxy-pcl-ros

4. Build

4.1 Clone repository:

cd ~/colcon_ws/src
git clone https://github.com/chris7462/floam.git
cd ..
colcon build
source ./install/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

Then convert the ROS1 bag to ROS2 bag. See here for reference

4.3 Launch ROS

ros2 launch floam floam.launch.py

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

ros2 launch floam floam_mapping.launch.py

If the mapping process is slow, you may wish to change the rosbag speed by replacing "-r 0.5" with "-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. Acknowledgements

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

7. 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={}
}