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https://github.com/wh200720041/floam
Fast LOAM: Fast and Optimized Lidar Odometry And Mapping for indoor/outdoor localization IROS 2021
https://github.com/wh200720041/floam
aloam loam localization robotics slam
Last synced: 24 days ago
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Fast LOAM: Fast and Optimized Lidar Odometry And Mapping for indoor/outdoor localization IROS 2021
- Host: GitHub
- URL: https://github.com/wh200720041/floam
- Owner: wh200720041
- License: other
- Created: 2020-04-30T02:56:36.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-07-30T19:32:24.000Z (3 months ago)
- Last Synced: 2024-10-01T23:03:20.616Z (about 1 month ago)
- Topics: aloam, loam, localization, robotics, slam
- Language: C++
- Homepage:
- Size: 25.4 MB
- Stars: 1,057
- Watchers: 24
- Forks: 274
- Open Issues: 44
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-SLAM - Fast LOAM
- awesome-robotic-tooling - Fast LOAM - Fast and Optimized Lidar Odometry And Mapping. (Simultaneous Localization and Mapping / Lidar)
README
# 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](https://github.com/laboshinl/loam_velodyne) and [A-LOAM](https://github.com/HKUST-Aerial-Robotics/A-LOAM) .
**Modifier:** [Wang Han](http://wanghan.pro), Nanyang Technological University, Singapore
## 1. Demo Highlights
Watch our demo at [Video Link](https://youtu.be/PzZly1SQtng)
## 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.
ROS Noetic. [ROS Installation](http://wiki.ros.org/ROS/Installation)
### 3.2. **Ceres Solver**
Follow [Ceres Installation](http://ceres-solver.org/installation.html).
Note that starting from Ceres 2.1, GPU can be used to speed up optimization
### 3.3. **PCL**
Follow [PCL Installation](http://www.pointclouds.org/downloads/linux.html).
### 3.4. **Trajectory visualization**
For visualization purpose, this package uses hector trajectory sever, you may install the package by
```
sudo apt-get install ros-noetic-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](https://drive.google.com/file/d/1eyO0Io3lX2z-yYsfGHawMKZa5Z0uYJ0W/view?usp=sharing) or [KITTI sequence 07](https://drive.google.com/file/d/1_qUfwUw88rEKitUpt1kjswv7Cv4GPs0b/view?usp=sharing)
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](https://github.com/ethz-asl/kitti_to_rosbag) or [kitti2bag](https://github.com/tomas789/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-noetic-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](https://github.com/HKUST-Aerial-Robotics/A-LOAM) and LOAM(J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time) and [LOAM_NOTED](https://github.com/cuitaixiang/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={}
}
```