Direct LiDAR Odometry: Fast Localization with Dense Point Clouds

Last update: Dec 30, 2022

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Overview

Direct LiDAR Odometry: Fast Localization with Dense Point Clouds

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.

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:

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 package (or catkin_make if preferred):

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:

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 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.

Test Data

For your convenience, we provide example test data here (9 minutes, ~4.2GB). To run, first launch DLO (with default point cloud and IMU topics) via:

roslaunch direct_lidar_odometry dlo.launch

In a separate terminal session, play back the downloaded bag:

rosbag play dlo_test.bag

Citation

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

@article{chen2021direct,
  title={Direct LiDAR Odometry: Fast Localization with Dense Point Clouds},
  author={Chen, Kenny and Lopez, Brett T and Agha-mohammadi, Ali-akbar and Mehta, Ankur},
  journal={arXiv preprint arXiv:2110.00605},
  year={2021}
}

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.

License

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

Comments

Coordinate system conversion

Hi, thanks for your great work. I used my lidar and imu to record the data set and run it in DLO. The following figure is the map generated by the algorithm. I found that IMU drift is very serious. Is this related to coordinate system transformation? How can I set up and configure files to improve this situation?

opened by HomieRegina 15
HI, I use the nanoicp to location, but it cann`t work

Thanks for your work, I want to use nanoicp to location, it cann`t work right, but I use the pcl Gicp， it can work, so, there is anything i need to do?

opened by tust13018211 10
Map generation

Hi, How is the map generated? What algorithm is used? Can we upload a pcd map of the environment and then apply dlo just for localisation? I assumed dlo as a localisation algorithm, but it seems to take in the point cloud data from the bag file and generate a map on its own.

opened by Srichitra-S 9
gicp speed

I run source code each scan's gicp process just cost 2-3 ms, when i run my own code use gicp library it cost 100-200ms. Both Input cloud's size almost same, which setting options may cause this phenomenon?

opened by yst1 8
how to localize on a given pcd map?

i have a trouble understanding how to localise on a given 3d pcd map, can anyone explain the steps

i have a pcd map which i can load into rviz, and i have lidar-points and imu data, then how to use this package for localization?

opened by srinivasrama 6
jacobian

Hello author, I want to derive the jacobian in the code. I see we use global perturbation to update. When i try this, i meet this problem to look for your help. When using Woodbury matrix identity it seems to make the formula more complicated, so i'm stuck to look for your help. Thanks for your help in advance!

Best regards Xiaoliang jiao

opened by narutojxl 6
the transformation between your lidar and imu?

I use a vlp16 lidar and a microstran imu(3DM-GX5-25) to run your program. But I failed. I think my transformation between imu and lidar is different from yours. So could you tell your true transformation between imu and lidar. the following pitcure is my configuration: Thank you very much!

opened by nonlinear1 6
Conversion of lidar and IMU coordinate system

Hi! I use a vlp16 lidar and a microstran imu(3DM-GX5-25) to record bags. And here is the part of the map I built with your algorithm. Because the coordinate systems of IMU and lidar are not consistent in the physical direction, this has affected the construction of the two-layer environment. Could you please tell me how to convert IMU and lidar coordinate system?

opened by HomieRegina 4
some trivial questions
Hello authors, I have some trivial questions to look for your help. Thanks for your help.

diff rot is neither this->rotq.inverse() * closest_pose_r nor closest_pose_r.inverse() * this->rotq?

quaternion to axis angle, rotation norm is theta_rad *=2; ?

this->keyframe_cloud is the last keyframe, we should set curr keyframe into gicp_s2s.setInputSource(), before we calculate covariance, namely this->keyframe_cloud update should before we calculate covariance ?

returned final rotation should be check whether to flip sign, rather than in publishPose() ?
opened by narutojxl 4
Coordinate Frame

Hi,

Thank you for sharing this great work.

I've set of problem such as defining the coordinate system of the odometry . Which coordinate frame do you use for odometry NED or BODY. I thought you use NED coordinate frame so that I expected when I move Lidar + IMU to the North direction, x should be positive increased but it has not been stable behaviour. I also moved the system to the East direction it should be caused positive increase on y axis but same problem. I think you use BODY frame or something different coordinate frame for odometry.

I wonder also how do you send odometry information to the autopilot of drone. I prefered mavros and selected the related odometry ros topic and send them without change so I saw the odometry message in the autopilot but when odometry receive to the autopilot of drone it looks like x and y exchanged in autopilot even they are not exchanged odometry output of direct lidar odometry.

Summary of the Question is that which coordinate frame do you use for odometry ? How can I set up the odometry frame to NED? x and y are negative decreased when I move lidar+imu to the North and the East respectively. and Z looks like 180 reversed. Should I multiply x and y with minus or apply rotation matrix to fix negative decreasing x, y odometry ?

How can I ensure that autopilot and your odometry coordinate plane are in the same coordinate plane ?

opened by danieldive 3
How to correct point cloud caused by motion？

Hi, thank you for your great work about DLO. I have a question. When the robot is moving fast, the lidar point cloud will generate distortion due to movement, which will cause negative impact on the construction of the map. So how did DLO deal with this problem?

opened by JACKLiuDay 2

Releases(v1.4.2)

v1.4.2(Jul 15, 2022)
Features

Option to publish only keyframes instead of the full map to reduce RViz delays (turn up Decay Time to see full map)

Bugfixes

N/A
Source code(tar.gz)
Source code(zip)
v1.4.1(Jun 30, 2022)
Features

N/A

Bugfixes

Patch for ARM architecture (e.g., Raspberry Pi, NVIDIA Jetsons)

Source code(tar.gz)
Source code(zip)
v1.4.0(Jun 20, 2022)
Features

New ROS service to save generated map into a .pcd file

Bugfixes

N/A
Source code(tar.gz)
Source code(zip)
v1.3.1(Apr 11, 2022)
Features

N/A

Bugfixes

Rotational norm when converting from quaternion to axis-angle should be multiplied by 2

Update keyframe cloud prior to calculating covariances

Credit: @narutojxl via #8
Source code(tar.gz)
Source code(zip)
v1.3.0(Apr 11, 2022)
Features

N/A

Bugfixes

Fixed a bug during submap keyframe extraction

Uses a more up-to-date positional estimate during submap keyframe search

Credit: @narutojxl via #7
Source code(tar.gz)
Source code(zip)
v1.2.0(Jan 12, 2022)
Features

Speed improvements

Bugfixes

Fixed covariance propagation for submap

Credit: @RyGuy101
Source code(tar.gz)
Source code(zip)
v1.1.0(Jan 4, 2022)
Features

Parameter to initialize DLO with a known pose

Bugfixes

N/A
Source code(tar.gz)
Source code(zip)
v1.0.0(Dec 7, 2021)

Initial release
Source code(tar.gz)
Source code(zip)