Active Graph SLAM Project

Code used for the papers "Fast Autonomous Robotic Exploration Using the Underlying Graph Structure" and "A General Relationship between Optimality Criteria and Connectivity Indices for Active Graph-SLAM".

Tested by jplaced for Ubuntu 20.04, ROS Noetic.

Citation

• Placed, J. A., & Castellanos, J. A. (2022). A General Relationship between Optimality Criteria and Connectivity Indices for Active Graph-SLAM. IEEE Robotics and Automation Letters, vol. 8, no. 2, pp. 816-823, doi: 10.1109/LRA.2022.3233230.
• Placed, J. A., & Castellanos, J. A. (2021). Fast Autonomous Robotic Exploration Using the Underlying Graph Structure. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 6672-6679), doi: 10.1109/IROS51168.2021.9636148

Dependencies:

• Python3 (numpy, networkx, matplotlib, scikit-learn)
• ROS Noetic & Gazebo
• OpenCV: https://opencv.org/
• Eigen3: https://eigen.tuxfamily.org/dox/GettingStarted.html
• g2o: Downloand and install from https://github.com/RainerKuemmerle/g2o.
• python3 catkin tools (sudo apt-get install python3-catkin-tools)
• SuiteSparse (sudo apt-get install libsuitesparse-dev)

Contained in 3d party folder working in ROS Noetic:

• kobuki_plugins
• open_karto

Build:

1. Clone repository
2. Build:

cd active_graph_slam

catkin b

4. Edit path where graphs are saved/read, in graph_d_exploration/param/mapping_karto_g2o.yaml and graph_d_exploration/scripts/constants.py

Run simulation:

1. First source workspace:

source active_graph_slam/devel/setup.bash

2. Launch Gazebo simulator, RViZ and SLAM algorithm:

roslaunch graph_d_exploration single_willow.launch

3. Launch Active Decision Maker:

roslaunch graph_d_exploration graph_dopt.launch