The Pomona 3D Graph Slam online environment mapping project aims to generate the map indoor and outdoor environments based on 3D Graph SLAM with NDT scan matching-based odometry estimation and loop detection. In this project, we utilizes the GPS, IMU to correct the pose graph and compare the performance of mapping between these sensors.
on melodic & noetic
- Set ROS parameter for simulated time:
- Open a terminal and set the use_sim_time parameter to true. This is required for working with bag files, as it allows ROS to use simulated time:
rosparam set use_sim_time true- Launch hdl_graph_slam nodes:
- Open a new terminal and launch the HDL Graph SLAM nodes using the provided launch file:
- For indoor environment:
roslaunch hdl_graph_slam hdl_graph_slam_501.launch- For outdoor environment:
roslaunch hdl_graph_slam_400.launch- Open RViz for visualization:
- In a new terminal, navigate to the rviz configuration file for HDL Graph SLAM:
roscd hdl_graph_slam/rviz- Now, start RViz with the hdl_graph_slam configuration file:
rviz -d hdl_graph_slam.rviz- Play the bag file:
- In a new terminal, start playing the filtered bag file with the rosbag play command. The --clock option is used to sync the playback with the simulated time set earlier:
rosbag play --clock rosbag_file- 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.
- Enter the catkin workspace:
cd ep_ws/ source /devel/setup.bash
- Launch hdl_graph_slam nodes
roslaunch hdl_graph_slam hdl_graph_slam_full_6dec_new.launch
- Open RViz for visualization:
- In a new terminal, navigate to the rviz configuration file for HDL Graph SLAM:
roscd hdl_graph_slam/rviz- Now, start RViz with the hdl_graph_slam configuration file:
rviz -d hdl_graph_slam_6dec_new.rvizIn this project, we utilized these following contraints to generate the map.
-
Loop closure
-
GPS
- /gnss (gps from pomona toolkit)
-
IMU acceleration (gravity vector)
- /imu/imu_data (sensor_msgs/Imu from pomona toolkit)
-
Floor plane
- /floor_detection/floor_coeffs (hdl_graph_slam/FloorCoeffs)
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 for the accumulated rotation error of the scan matching in large flat indoor environments.
All the configurable parameters are listed in launch/hdl_graph_slam.launch as ros params.
This project is running based on the hdl_graph_slam package that requires the following libraries:
- OpenMP
- PCL
- g2o
- suitesparse
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 ProgressBar2Bag file (recorded in CLC building at ELTE):
- lidar_indoor.bag (raw data, 344MB)
rosparam set use_sim_time true
roslaunch hdl_graph_slam hdl_graph_slam_501.launchroscd hdl_graph_slam/rviz
rviz -d hdl_graph_slam.rvizrosbag play --clock hdl_501_filtered.bagWe 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.bagYou'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'"In this example, we solely captured lidar data in an outdoor setting. The outcome shows distortion and overlapping between the lidar scans. A superior outcome was achieved when comparing it to example 3, where GPS and IMU data were integrated to elevate the pose graph's performance.
Bag file (recorded in an outdoor environment):
- lidar_outdoor.bag (raw data, about 900MB)
rosparam set use_sim_time true
roslaunch hdl_graph_slam hdl_graph_slam_400.launchroscd hdl_graph_slam/rviz
rviz -d hdl_graph_slam.rvizrosbag play --clock hdl_400.bag
In this example, we utilize the GPS data and IMU data to correct the pose graph. As depicted in the images, the building is accurately represented without distortion, providing a clear view of the environment.
