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[IEEE RA-L & ICRA'22] A lightweight and computationally-efficient frontend LiDAR odometry solution with consistent and accurate localization.
| Date | Stars |
|---|---|
| 2026-07-24 | 1039 |
| 2026-07-25 | 1039 |
| 2026-07-28 | 1039 |
| 2026-07-30 | 1039 |
| 2026-08-06 | 1039 |
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# Direct LiDAR Odometry: <br> Fast Localization with Dense Point Clouds
#### [[IEEE RA-L](https://ieeexplore.ieee.org/document/9681177)] [[ArXiv](https://arxiv.org/abs/2110.00605)] [[Video](https://www.youtube.com/watch?v=APot6QP_wvg)] [[Code](https://github.com/vectr-ucla/direct_lidar_odometry)]
DLO is a lightweight and computationally-efficient frontend LiDAR odometry solution with consistent and accurate localization. It features several algorithmic innovations that increase speed, accuracy, and robustness of pose estimation in perceptually-challenging environments and has been extensively tested on aerial and legged robots.
This work was part of NASA JPL Team CoSTAR's research and development efforts for the DARPA Subterranean Challenge, in which DLO was the primary state estimation component for our fleet of autonomous aerial vehicles.
<br>
<p align='center'>
<img src="./doc/img/aquila.png" alt="drawing" width="320"/>
<img src="./doc/img/spot.png" alt="drawing" width="320"/>
</p>
<p align="center">
<img src="./doc/gif/aquila.gif" alt="drawing" width="360"/>
<img src="./doc/gif/spot.gif" alt="drawing" width="360"/>
</p>
<p align="center">
<img src="./doc/gif/dlo.gif" alt="drawing" width="720"/>
</p>
## Instructions
DLO requires an input point cloud of type `sensor_msgs::PointCloud2` with an optional IMU input of type `sensor_msgs::Imu`. Note that although IMU data is not required, it can be used for initial gravity alignment and will help with point cloud registration.
### Dependencies
Our system has been tested extensively on both Ubuntu 18.04 Bionic with ROS Melodic and Ubuntu 20.04 Focal with ROS Noetic, although other versions may work. The following configuration with required dependencies has been verified to be compatible:
- Ubuntu 18.04 or 20.04
- ROS Melodic or Noetic (`roscpp`, `std_msgs`, `sensor_msgs`, `geometry_msgs`, `pcl_ros`)
- C++ 14
- CMake >= `3.16.3`
- OpenMP >= `4.5`
- Point Cloud Library >= `1.10.0`
- Eigen >= `3.3.7`
Installing the binaries from Aptitude should work though:
```sh
sudo apt install libomp-dev libpcl-dev libeigen3-dev
```
### Compiling
Create a catkin workspace, clone the `direct_lidar_odometry` repository into the `src` folder, and compile via the [`catkin_tools`](https://catkin-tools.readthedocs.io/en/latest/) package (or [`catkin_make`](http://wiki.ros.org/catkin/commands/catkin_make) if preferred):
```sh
mkdir ws && cd ws && mkdir src && catkin init && cd src
git clone https://github.com/vectr-ucla/direct_lidar_odometry.git
catkin build
```
### Execution
After sourcing the workspace, launch the DLO odometry and mapping ROS nodes via:
```sh
roslaunch direct_lidar_odometry dlo.launch \
pointcloud_topic:=/robot/velodyne_points \
imu_topic:=/robot/vn100/imu
```
Make sure to edit the `pointcloud_topic` and `imu_topic` input arguments with your specific topics. If an IMU is not being used, set the `dlo/imu` ROS param to `false` in `cfg/dlo.yaml`. However, if IMU data is available, please allow DLO to calibrate and gravity align for three seconds before moving. Note that the current implementation assumes that LiDAR and IMU coordinate frames coincide, so please make sure that the sensors are physically mounted near each other.
If successful, RViz will open and you will see similar terminal outputs to the following:
<p align='center'>
<img src="./doc/img/imu_calibration.png" alt="drawing" width="400"/>
<img src="./doc/img/terminal_output.png" alt="drawing" width="400"/>
</p>
### Services
To save DLO's generated map into `.pcd` format, call the following service:
```sh
rosservice call /robot/dlo_map/save_pcd LEAF_SIZE SAVE_PATH
```
To save the trajectory in KITTI format, call the following service:
```sh
rosservice call /robot/dlo_odom/save_traj SAVE_PATH
```
### Test Data
For your convenience, we provide example test data [here](https://ucla.box.com/shared/static/ziojd3auzp0zzcgwb1ucau9anh69xwv9.bag) (9 minutes, ~4.2GB). To run, first launcExcerpt of 5,726 characters
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matched fp:7f4c71edb3c25fde, topic:robotics, topic:ros, topic:slam