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https://github.com/koide3/hdl_localization
Real-time 3D localization using a (velodyne) 3D LIDAR
https://github.com/koide3/hdl_localization
lidar localization real-time ros velodyne
Last synced: 4 months ago
JSON representation
Real-time 3D localization using a (velodyne) 3D LIDAR
- Host: GitHub
- URL: https://github.com/koide3/hdl_localization
- Owner: koide3
- License: bsd-2-clause
- Created: 2018-01-19T12:03:08.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-06-15T10:58:25.000Z (about 1 year ago)
- Last Synced: 2023-11-07T12:32:05.671Z (8 months ago)
- Topics: lidar, localization, real-time, ros, velodyne
- Language: C++
- Size: 8.42 MB
- Stars: 695
- Watchers: 30
- Forks: 289
- Open Issues: 59
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- Awesome-Autonomous-Driving - hdl_localization - **Lidar + IMU** 基于卡尔曼滤波的位置估计使用了激光雷达，IMU, 可以做到实时估计。 (5 Localization and SLAM / 3.4 High Performance Inference)
- awesome-stars - koide3/hdl_localization - Real-time 3D localization using a (velodyne) 3D LIDAR (C++)
README
# hdl_localization
***hdl_localization*** is a ROS package for real-time 3D localization using a 3D LIDAR, such as velodyne HDL32e and VLP16. This package performs Unscented Kalman Filter-based pose estimation. It first estimates the sensor pose from IMU data implemented on the LIDAR, and then performs multi-threaded NDT scan matching between a globalmap point cloud and input point clouds to correct the estimated pose. IMU-based pose prediction is optional. If you disable it, the system uses the constant velocity model without IMU information.
Video:
[](https://youtu.be/1EyF9kxJOqA)
[](https://travis-ci.org/koide3/hdl_global_localization)
## Requirements
***hdl_localization*** requires the following libraries:
- PCL
- OpenMP
The following ros packages are required:
- pcl_ros
- [ndt_omp](https://github.com/koide3/ndt_omp)
- [fast_gicp](https://github.com/SMRT-AIST/fast_gicp)
- [hdl_global_localization](https://github.com/koide3/hdl_global_localization)
## Installation
```bash
cd /your/catkin_ws/src
git clone https://github.com/koide3/ndt_omp
git clone https://github.com/SMRT-AIST/fast_gicp --recursive
git clone https://github.com/koide3/hdl_localization
git clone https://github.com/koide3/hdl_global_localization
cd /your/catkin_ws
catkin_make -DCMAKE_BUILD_TYPE=Release
# if you want to enable CUDA-accelerated NDT
# catkin_make -DCMAKE_BUILD_TYPE=Release -DBUILD_VGICP_CUDA=ON
```
### Support docker :whale:
Using docker, you can conveniently satisfy the requirement environment.
Please refer to the repository below and use the docker easily.
- [Taeyoung96/hdl_localization_tutorial](https://github.com/Taeyoung96/hdl_localization_tutorial)
## Parameters
All configurable parameters are listed in *launch/hdl_localization.launch* as ros params.
The estimated pose can be reset using using "2D Pose Estimate" on rviz
## Topics
- ***/odom*** (nav_msgs/Odometry)
- Estimated sensor pose in the map frame
- ***/aligned_points***
- Input point cloud aligned with the map
- ***/status*** (hdl_localization/ScanMatchingStatus)
- Scan matching result information (e.g., convergence, matching error, and inlier fraction)
## Services
- ***/relocalize*** (std_srvs/Empty)
- Reset the sensor pose with the global localization result
- For details of the global localization method, see [hdl_global_localization](https://github.com/koide3/hdl_global_localization)
## Example
Example bag file (recorded in an outdoor environment): [hdl_400.bag.tar.gz](http://www.aisl.cs.tut.ac.jp/databases/hdl_graph_slam/hdl_400.bag.tar.gz) (933MB)
```bash
rosparam set use_sim_time true
roslaunch hdl_localization hdl_localization.launch
```
```bash
roscd hdl_localization/rviz
rviz -d hdl_localization.rviz
```
```bash
rosbag play --clock hdl_400.bag
```
```bash
# perform global localization
rosservice call /relocalize
```
If it doesn't work well or the CPU usage is too high, change *ndt_neighbor_search_method* in *hdl_localization.launch* to "DIRECT1". It makes the scan matching significantly fast, but a bit unstable.
## Related packages
- [interactive_slam](https://github.com/koide3/interactive_slam)
- hdl_graph_slam
- hdl_localization
- hdl_global_localization
- hdl_people_tracking
Kenji Koide, Jun Miura, and Emanuele Menegatti, A Portable 3D LIDAR-based System for Long-term and Wide-area People Behavior Measurement, Advanced Robotic Systems, 2019 [[link]](https://www.researchgate.net/publication/331283709_A_Portable_3D_LIDAR-based_System_for_Long-term_and_Wide-area_People_Behavior_Measurement).
## Contact
Kenji Koide, [email protected]
Active Intelligent Systems Laboratory, Toyohashi University of Technology, Japan [\[URL\]](http://www.aisl.cs.tut.ac.jp)
Human-Centered Mobility Research Center, National Institute of Advanced Industrial Science and Technology, Japan [\[URL\]](https://unit.aist.go.jp/rirc/en/team/smart_mobility.html)