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koide3 / hdl_graph_slam

3D LIDAR-based Graph SLAM
BSD 2-Clause "Simplified" License
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hdl-graph-slam lidar point-cloud ros rslidar slam velodyne

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New SLAM package is released

A new 3D SLAM package is released: https://github.com/koide3/glim.

hdl_graph_slam

hdl_graph_slam is an open source ROS package for real-time 6DOF SLAM using a 3D LIDAR. It is based on 3D Graph SLAM with NDT scan matching-based odometry estimation and loop detection. It also supports several graph constraints, such as GPS, IMU acceleration (gravity vector), IMU orientation (magnetic sensor), and floor plane (detected in a point cloud). We have tested this package with Velodyne (HDL32e, VLP16) and RoboSense (16 channels) sensors in indoor and outdoor environments.

Build on melodic & noetic

Third-party extensions

See also the following nice works built upon hdl_graph_slam. Feel free to request to include your work in the list :)

Nodelets

hdl_graph_slam consists of four nodelets.

The input point cloud is first downsampled by prefiltering_nodelet, and then passed to the next nodelets. While scan_matching_odometry_nodelet estimates the sensor pose by iteratively applying a scan matching between consecutive frames (i.e., odometry estimation), floor_detection_nodelet detects floor planes by RANSAC. The estimated odometry and the detected floor planes are sent to hdl_graph_slam. To compensate the accumulated error of the scan matching, it performs loop detection and optimizes a pose graph which takes various constraints into account.

Constraints (Edges)

You can enable/disable each constraint by changing params in the launch file, and you can also change the weight (*_stddev) and the robust kernel (*_robust_kernel) of each constraint.

hdl_graph_slam supports several GPS message types. All the supported types contain (latitude, longitude, and altitude). hdl_graph_slam converts them into the UTM coordinate, and adds them into the graph as 3D position constraints. If altitude is set to NaN, the GPS data is treated as a 2D constrait. GeoPoint is the most basic one, which consists of only (lat, lon, alt). Although NavSatFix provides many information, we use only (lat, lon, alt) and ignore all other data. If you're using HDL32e, you can directly connect hdl_graph_slam with velodyne_driver via /gpsimu_driver/nmea_sentence.

This constraint rotates each pose node so that the acceleration vector associated with the node becomes vertical (as the gravity vector). This is useful to compensate for accumulated tilt rotation errors of the scan matching. Since we ignore acceleration by sensor motion, you should not give a big weight for this constraint.

This constraint optimizes the graph so that the floor planes (detected by RANSAC) of the pose nodes becomes the same. This is designed to compensate the accumulated rotation error of the scan matching in large flat indoor environments.

Parameters

All the configurable parameters are listed in launch/hdl_graph_slam.launch as ros params.

Services

Requirements

hdl_graph_slam requires the following libraries:

The following ROS packages are required:

# for melodic
sudo apt-get install ros-melodic-geodesy ros-melodic-pcl-ros ros-melodic-nmea-msgs ros-melodic-libg2o
cd catkin_ws/src
git clone https://github.com/koide3/ndt_omp.git -b melodic
git clone https://github.com/SMRT-AIST/fast_gicp.git --recursive
git clone https://github.com/koide3/hdl_graph_slam

cd .. && catkin_make -DCMAKE_BUILD_TYPE=Release

# for noetic
sudo apt-get install ros-noetic-geodesy ros-noetic-pcl-ros ros-noetic-nmea-msgs ros-noetic-libg2o

cd catkin_ws/src
git clone https://github.com/koide3/ndt_omp.git
git clone https://github.com/SMRT-AIST/fast_gicp.git --recursive
git clone https://github.com/koide3/hdl_graph_slam

cd .. && catkin_make -DCMAKE_BUILD_TYPE=Release

[optional] bag_player.py script requires ProgressBar2.

sudo pip install ProgressBar2

Example1 (Indoor)

Bag file (recorded in a small room):

rosparam set use_sim_time true
roslaunch hdl_graph_slam hdl_graph_slam_501.launch
roscd hdl_graph_slam/rviz
rviz -d hdl_graph_slam.rviz
rosbag play --clock hdl_501_filtered.bag

We also provide bag_player.py which automatically adjusts the playback speed and processes data as fast as possible.

rosrun hdl_graph_slam bag_player.py hdl_501_filtered.bag

You'll see a point cloud like:

You can save the generated map by:

rosservice call /hdl_graph_slam/save_map "resolution: 0.05
destination: '/full_path_directory/map.pcd'"

Example2 (Outdoor)

Bag file (recorded in an outdoor environment):

rosparam set use_sim_time true
roslaunch hdl_graph_slam hdl_graph_slam_400.launch
roscd hdl_graph_slam/rviz
rviz -d hdl_graph_slam.rviz
rosbag play --clock hdl_400.bag

Example with GPS

Ford Campus Vision and Lidar Data Set [URL]

The following script converts the Ford Lidar Dataset to a rosbag and plays it. In this example, hdl_graph_slam utilizes the GPS data to correct the pose graph.

cd IJRR-Dataset-2
rosrun hdl_graph_slam ford2bag.py dataset-2.bag
rosrun hdl_graph_slam bag_player.py dataset-2.bag

Use hdl_graph_slam in your system

  1. Define the transformation between your sensors (LIDAR, IMU, GPS) and base_link of your system using static_transform_publisher (see line #11, hdl_graph_slam.launch). All the sensor data will be transformed into the common base_link frame, and then fed to the SLAM algorithm.

  2. Remap the point cloud topic of prefiltering_nodelet. Like:

  <node pkg="nodelet" type="nodelet" name="prefiltering_nodelet" ...
    <remap from="/velodyne_points" to="/rslidar_points"/>
  ...

Common Problems

Parameter tuning guide

The mapping quality largely depends on the parameter setting. In particular, scan matching parameters have a big impact on the result. Tune the parameters accoding to the following instructions:

License

This package is released under the BSD-2-Clause License.

Note that the cholmod solver in g2o is licensed under GPL. You may need to build g2o without cholmod dependency to avoid the GPL.

Related packages

Papers

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, https://staff.aist.go.jp/k.koide

Active Intelligent Systems Laboratory, Toyohashi University of Technology, Japan [URL]
Mobile Robotics Research Team, National Institute of Advanced Industrial Science and Technology (AIST), Japan [URL]