OGM2PGBM: Occupancy Grid Map to Pose Graph-based Map for long-term 2D LiDAR-based localization

This repo contains the following two applications:

OGM2PGBM: generate pose graph-based maps on 2D occupancy grid maps, which can be created from a TLS Point cloud or a BIM/CAD model. This pose graph-based maps can be used for accurate localization in changing and dynamic environments, as demostrated in our paper.

The following animation shows an overview of the method and compares AMCL and Cartographer; the latter can be applied after leveraging the Ogm2Pgbm package. It is clear that for AMCL is more difficult to keep track of the pose of the robot in comparison with the performance of Cartographer.

GMCL & CARTO/SLAM_toolbox: conbine the fast global localization feature of GMCL with the more accurate pose tracking performance of Cartographer/SLAM_toolbox

Additionally, it includes the packages amcl, gmcl, cartographer and slam_toolbox, so that they can be used and compared with a bagfile that should be located in the mounted directory ~/workspace easily.

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Requirements

OGM2PGBM
- Principle
- Running the code
  - Args
  - Step by step
  - Note
GMCL & CARTO/SLAM_toolbox
Citation
Reference projects

Requirements

If you plan to use our docker container, with all the methods installed (Warning: it requires >5 GB of space) you only need to install docker.

If you don't want to use docker and only want to use the Ogm2Pgbm package, you can install the content of this folder and use it as a normal catkin package.

If you still want to install all the different localization methods, you can have a look at the docker file and install the respective dependencies on your local machine.

OGM2PGBM

Principle

The workflow of OGM2PGBM is as follows, see the function new_map_callback(self, grid_map) for details:

Subscribe map from the map topic
Skeletonize the map and get its voronoi waypoint (see self.skeletonize())
Perform a coverage path planning on it (see self.CPP())
- Extract the farest endpoint pair first as the start and goal point
- Then dilate it with a 2x2 kernel to bold the centerline
- Do CPP wavefront algorithm and get the sorted waypoint
Do a raytracing on the waypoints one by one, and publish the /laserscan topic (see self.raytracer())

It produces /tf, /clock, /odom, /scan topics with frame robot_map, robot_odom and robot_base_link.

Since /tf is needed, python2.7 is used in this script.

Running the code

Args

This package is a standard ros package and can be launched with roslaunch command.

args:

map_file: default value is /root/workspace/map/OGM_empty.pgm.yaml
record: default value is false, the recorded bag can be found at /root/.ros/ogm2pgbm_sensordata.bag

Step by step

First clone the repository.

git clone https://github.com/MigVega/Ogm2Pgbm.git
cd Ogm2Pgbm

With the launch file, we only need three steps to generate the base pbstream/posegraph based-map.

run into the docker

bash autorun.sh

Note: If you get the error docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]]. install nvidia-container-toolkit with the following command sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit

launch the application with or without the args

roslaunch ogm2pgbm ogm2pgbm.launch map_file:=/root/workspace/map/OGM_empty.pgm.yaml record:=true

The target bag file will be stored under /root/.ros/ogm2pgbm_sensordata.bag. By default, the demo bag will also be copied into this place. So you can also skip the second step if you want.

After generating bagfiles, use Cartographer to generate pbstream or SLAM toolbox to generate posegraph maps. With the following command Cartographer will run in offline mode, which will generate pbstream quite fast, but without any visual output in rviz.

roslaunch cartographer_ros ogm2pgbm_my_robot.launch bag_filename:=/root/.ros/ogm2pgbm_sensordata.bag

You can also launch Slam_toolbox. (There will be some error report in the terminal, just ignore them and wait for some seconds.)

roslaunch slam_toolbox ogm2pgbm.launch bag_filename:=/root/.ros/ogm2pgbm_sensordata.bag

The target pbstream file will be generated automatically at /root/.ros/ogm2pgbm_sensordata.bag.pbstream after . For slam_toolbox, you also need to click on the serialization button on the rviz plugin. The target files are also located at /root/.ros.

Note

As it is already a package managed by catkin, if you have changed the src code or configuation inside the container, you need to do the following instructions to compile and install the package and source the env.

catkin_make_isolated --install --use-ninja --pkg ogm2pgbm
source install_isolated/setup.bash

All the node will be closed when the rviz is closed, including the rosbag node
The parameters of cartographer when using OGM2PGBM scripts to generate pbstream are as follows (see cartographer_ros/configuration_files/ogm2pgbm_my_robot.lua)

remap /scan or /odom in launch file if needed

GMCL & CARTO/SLAM_toolbox

This project combines the pros of the two algorithms, using the fast global localization feature of GMCL and the accurate pose tracking performance of Cartographer or SLAM toolbox.

First, change the bagname in the line 11 of the file ~/catkin_ws/src/gmcl_carto/gmcl_carto.py.
Then, make sure the bagfile is located in the directory /root/workspace.
At last, run the script directly python ~/catkin_ws/src/gmcl_carto/gmcl_carto.py.
Do the same if you want to run gmcl_carto/gmcl_slamtoolbox.py instead.

Citation

If you use this library for an academic work, please cite the original paper.

@inproceedings{ vega:2022:2DLidarLocalization,
	author = {Vega Torres, M.A. and Braun, A. and Borrmann, A.},
	title = {Occupancy Grid Map to Pose Graph-based Map: Robust BIM-based 2D- LiDAR Localization for Lifelong Indoor Navigation in Changing and Dynamic Environments},
	booktitle = {eWork and eBusiness in Architecture, Engineering and Construction: ECPPM 2022},
	editor = {Eilif Hjelseth, Sujesh F. Sujan, and Raimar Scherer},
	publisher = {CRC Press},
	year = {2022},
	month = {Sep},
	url = {https://publications.cms.bgu.tum.de/2022_ECPPM_Vega.pdf},
	isbn = {978-1-003-35422-2},
	doi = {10.1201/9781003354222-72}
}

Please also cite the code:

daoran / Ogm2Pgbm