Awesome

New packages arrived

We released a new open source 3D mapping framework GLIM.
We also developed a map-based localization system GLIL as closed source.

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:<br>

Requirements

hdl_localization requires the following libraries:

• PCL
• OpenMP

The following ros packages are required:

• pcl_ros
• ndt_omp
• fast_gicp
• hdl_global_localization

Installation

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

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

Example

Example bag file (recorded in an outdoor environment): hdl_400.bag.tar.gz (933MB)

rosparam set use_sim_time true
roslaunch hdl_localization hdl_localization.launch

roscd hdl_localization/rviz
rviz -d hdl_localization.rviz

rosbag play --clock hdl_400.bag

# perform global localization
rosservice call /relocalize

<img src="data/figs/localization1.png" height="256pix" /> <img src="data/figs/localization2.png" height="256pix" />

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
• <a href="https://github.com/koide3/hdl_graph_slam">hdl_graph_slam</a>
• <a href="https://github.com/koide3/hdl_localization">hdl_localization</a>
• <a href="https://github.com/koide3/hdl_global_localization">hdl_global_localization</a>
• <a href="https://github.com/koide3/hdl_people_tracking">hdl_people_tracking</a>

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].

Contact

Kenji Koide, k.koide@aist.go.jp

Active Intelligent Systems Laboratory, Toyohashi University of Technology, Japan [URL] Human-Centered Mobility Research Center, National Institute of Advanced Industrial Science and Technology, Japan [URL]