<h1 align='center' style="text-align:center; font-weight:bold; font-size:2.0em;letter-spacing:2.0px;"> CBGL: Fast Monte Carlo Passive Global Localisation of 2D LIDAR Sensor </h1> <div align="center">

</div> <h3 align="center"> <a href="#inputoutput-at-a-glance">I/O</a> <span> · </span> <a href="#pre-installation">install</a> <span> · </span> <a href="#run">run</a> </h3>

cbgl is a ROS package written in C++ that allows you to localise your 2D LIDAR sensor in a given 2D occupancy grid map under global uncertainty in position and orientation, in minimal time

• You can expect the execution time to roughly have an order of magnitude of $10e \cdot \text{area} \cdot N_s$ microseconds, where $\text{area}$ is the area of the map's free space and $N_s$ is the LIDAR's number of rays. (Strictly speaking the execution time varies according to the geometry of the environment and other factors.) In the video below the environment area is $2000$ m$^2$ and localisation is performed in under four seconds

• CBGL does not require motion for performing global localisation: it's a one-shot approach that only requires a single laser scan measurement and the map of the sensor's environment

<!-- Click on the image for a brief demo [](https://youtu.be/DkKdxFNJG4g) -->

https://github.com/li9i/cbgl/assets/1658819/5794cd21-651d-4924-b453-25c46b9e42a9

_{A panoramic 2D LIDAR sensor mounted on a turtlebot 2 is spawned into an environment at a pose whose immediate surroundings are repeated in (almost) the same geometry and proportions at locations other than the sensor's spawning ground. The user calls the global localisation service once before moving the robot at a second challenging pose, at which she calls it for a second time. Both times cbgl is successful in estimating the sensor's pose. Sensor characteristics: $N_s = 360$ rays; noise: $\sim \mathcal{N} (0.0, 0.05^2)$ [m,m$^2$]}

Why use CBGL

<p align="center"> <img src="https://i.imgur.com/kAD8AmS.png?1"> </p>

Table of Contents

• Pre-installation
• Install
• Run
  • Launch
  • Call
• Input/output at a glance
• Motivation
• More results

Pre-installation

cbgl is installed, launched, and called via Docker:

• if this is your first time running docker then I happen to find this docker installation guide very friendly and easy to follow
• if instead you wish to install and run the package natively in Ubuntu 16.04, see the INSTALLATION_DEPRECATED.md guide.

Install

Build the image with the most recent code of this repository with:

git clone git@github.com:li9i/cbgl.git
cd cbgl
docker compose build

or pull it:

docker pull li9i/cbgl:latest

Run

Launch

If you cloned the repository then you may run the image via compose

cd cbgl
docker compose up

or, in any case, you may run the image with

docker run -it \
    --name=cbgl \
    --net=host \
    --rm \
    li9i/cbgl:latest

Call

Launching cbgl simply makes it go into stand-by mode and does not actually localise your sensor. To do so simply call the provided service

docker exec -it cbgl sh -c "source ~/catkin_ws/devel/setup.bash; rosservice call /robot/cbgl_node/global_localization"

Input/output at a glance

| i/o | Type | What | | --- | -------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | in | sensor_msgs/LaserScan | The scan message received via topic configuration_files/params_cbgl/scan_topic | | in | nav_msgs/OccupancyGrid | The map received via topic configuration_files/params_cbgl/map_topic | | out | geometry_msgs/PoseWithCovarianceStamped | The output pose published through topic configuration_files/params_cbgl/output_pose_topic | | out | Transform | The transform between the odom frame and the map frame if configuration_files/params_cbgl/tf_broadcast is set to true |

Motivation

<p align="center"> <img src="https://i.imgur.com/LQBwg7G.png"> </p>

More results

<p align="center"> <img src="https://i.imgur.com/0qt3chL.png"> </p>

Citation

The theoretical underpinning, key aspects, and experimental performance of CBGL are presented in the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems article cited through

@INPROCEEDINGS{10802235,
  author={Filotheou, Alexandros},
  booktitle={2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  title={CBGL: Fast Monte Carlo Passive Global Localisation of 2D LIDAR Sensor},
  year={2024},
  pages={3268-3275},
  doi={10.1109/IROS58592.2024.10802235}}