Direct LiDAR-Inertial Odometry: Lightweight LIO with Continuous-Time Motion Correction

[ IEEE ICRA ] [ arXiv ] [ Video ] [ Presentation ]

DLIO is a new lightweight LiDAR-inertial odometry algorithm with a novel coarse-to-fine approach in constructing continuous-time trajectories for precise motion correction. It features several algorithmic improvements over its predecessor, DLO, and was presented at the IEEE International Conference on Robotics and Automation (ICRA) in London, UK in 2023.

Instructions

Sensor Setup & Compatibility

DLIO has been extensively tested using a variety of sensor configurations and currently supports Ouster, Velodyne, Hesai, and Livox LiDARs. The point cloud should be of input type sensor_msgs::PointCloud2 and the 6-axis IMU input type of sensor_msgs::Imu.

For Livox sensors specifically, you can use the master branch directly if it is of type sensor_msgs::PointCloud2 (xfer_format: 0), or the feature/livox-support branch and the latest livox_ros_driver2 package if it is of type livox_ros_driver2::CustomMsg (xfer_format: 1) (see here for more information).

For best performance, extrinsic calibration between the LiDAR/IMU sensors and the robot's center-of-gravity should be inputted into cfg/dlio.yaml. If the exact values of these are unavailable, a rough LiDAR-to-IMU extrinsics can also be used (note however that performance will be degraded).

IMU intrinsics are also necessary for best performance, and there are several open-source calibration tools to get these values. These values should also go into cfg/dlio.yaml. In practice however, if you are just testing this work, using the default ideal values and performing the initial calibration procedure should be fine.

Also note that the LiDAR and IMU sensors need to be properly time-synchronized, otherwise DLIO will not work. We recommend using a LiDAR with an integrated IMU (such as an Ouster) for simplicity of extrinsics and synchronization.

Dependencies

The following has been verified to be compatible, although other configurations may work too:

• Ubuntu 20.04
• ROS Noetic (roscpp, std_msgs, sensor_msgs, geometry_msgs, nav_msgs, pcl_ros)
• C++ 14
• CMake >= 3.12.4
• OpenMP >= 4.5
• Point Cloud Library >= 1.10.0
• Eigen >= 3.3.7

sudo apt install libomp-dev libpcl-dev libeigen3-dev

DLIO supports ROS1 by default, and ROS2 using the feature/ros2 branch.

Compiling

Compile using the catkin_tools package via:

mkdir ws && cd ws && mkdir src && catkin init && cd src
git clone https://github.com/vectr-ucla/direct_lidar_inertial_odometry.git
catkin build

Execution

After compiling, source the workspace and execute via:

roslaunch direct_lidar_inertial_odometry dlio.launch \
  rviz:={true, false} \
  pointcloud_topic:=/robot/lidar \
  imu_topic:=/robot/imu

for Ouster, Velodyne, Hesai, or Livox (xfer_format: 0) sensors, or

roslaunch direct_lidar_inertial_odometry dlio.launch \
  rviz:={true, false} \
  livox_topic:=/livox/lidar \
  imu_topic:=/robot/imu

for Livox sensors (xfer_format: 1).

Be sure to change the topic names to your corresponding topics. Alternatively, edit the launch file directly if desired. If successful, you should see the following output in your terminal:

Services

To save DLIO's generated map into .pcd format, call the following service:

rosservice call /robot/dlio_map/save_pcd LEAF_SIZE SAVE_PATH

Test Data

For your convenience, we provide test data here (1.2GB, 1m 13s, Ouster OS1-32) of an aggressive motion to test our motion correction scheme, and here (16.5GB, 4m 21s, Ouster OSDome) of a longer trajectory outside with lots of trees. Try these two datasets with both deskewing on and off!

Citation

If you found this work useful, please cite our manuscript:

@article{chen2022dlio,
  title={Direct LiDAR-Inertial Odometry: Lightweight LIO with Continuous-Time Motion Correction},
  author={Chen, Kenny and Nemiroff, Ryan and Lopez, Brett T},
  journal={2023 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2023},
  pages={3983-3989},
  doi={10.1109/ICRA48891.2023.10160508}
}

Acknowledgements

We thank the authors of the FastGICP and NanoFLANN open-source packages:

• Kenji Koide, Masashi Yokozuka, Shuji Oishi, and Atsuhiko Banno, “Voxelized GICP for Fast and Accurate 3D Point Cloud Registration,” in IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2021, pp. 11 054–11 059.
• Jose Luis Blanco and Pranjal Kumar Rai, “NanoFLANN: a C++ Header-Only Fork of FLANN, A Library for Nearest Neighbor (NN) with KD-Trees,” https://github.com/jlblancoc/nanoflann, 2014.

We would also like to thank Helene Levy and David Thorne for their help with data collection.

Many thanks to @shrijitsingh99 for porting DLIO to ROS2!

License

This work is licensed under the terms of the MIT license.