3D Distributed Gas Source Localization and Mapping

Gas Distribution Mapping (GDM) consists in creating a map of a gas distribution to identify where and how the gas is dispersed in a given area. This technique has applications when situations like gas leaks, environmental emergencies and toxic chemical dispersion occur. Enabling robots to perform this task would provide a powerful tool to prevent dangerous situations and assist humans when emergencies arise.

This project focuses on the integration of quadcopters in GDM with the ultimate goal of producing a 3D map of a gaseous plume in an environment. Prior to this work, simulations and real time experiments focused on gas distribution mapping and localization exploiting mainly ground robots. Including quadcopters in these experimental settings is an important step to explore 3D mapping and localization algorithms and move towards experiments that are closer to real world scenarios.

Team and Collaborators

Research Period and Sponsors

This project started in October 2018.

SNSF is sponsoring this project for the duration of 4 years.

  • DISAL-SP175: David Ruegg, Path Planning for Gas Distribution Mapping
  • DISAL-SP163: Yasmine El Goumi, MultiRobot Gas Distribution Mapping and Source Localization in Simulation
  • DISAL-SP162: Shashank Deshmukh, Multi-Robot Gas Distribution Mapping in Simulation
  • DISAL-SP156: Nicolaj Schmid, Wind Estimation on a Quadrotor
  • DISAL-SP157: Thomas Peeters, Multi-Robot Gas Distribution Mapping
  • DISAL-SU31: Lixuan Tang, Design, Execution and Analysis of Experimental Campaign for 3D Gas Distribution Mapping
  • DISAL-IP39: Wanting Jin, Towards 3D Gas Source Localization in Realistic Indoor Environments using Micro Aerial Vehicles
  • DISAL-IP38: Mael Feurgard, Develop and effective Strategy for Gas Distribution Mapping and Source Localization using a Distributed Approach
  • DISAL-SP152: Lixuan Tang, 3D Gas Distribution Mapping in Simulation
  • DISAL-SP146: Mael Wildi, Odor Mapping with a Crazyflie and Static Sensor Nodes
  • DISAL-SP142: Ankita Humne, 3D Odor Distribution Mapping in Simulation.
  • DISAL-SP140: Abderrazzaq Moufidi, Validation of High-Fidelity Simulator for Odor Sensing with a Quadrotor
  • DISAL-SP138: Mehdi Akeddar, 3D Odor Distribution Mapping in Simulation
  • DISAL-SP131: Elise Jeandupeux, Dynamic Visualization of Real Data for 3D Odor Plume Mapping
  • DISAL-SP130: Costa Georgantas, Computational Fluid Dynamics to Assess the Impact of the Wake of a Quadcopter on a Plume of Gas
  • DISAL-SP123: Valentin Kindschi, Adapting a quadrotor for odor source localization in a realistic environment
  • DISAL-IP35: Chiara Ercolani, Integration of Quadcopters in Odor Distribution Mapping



GaSLAM: An Algorithm for Simultaneous Gas Source Localization and Gas Distribution Mapping in 3D

C. Ercolani; L. Tang; A. Martinoli 

2022.  2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, October 23-27, 2022. p. 333-340. DOI : 10.1109/IROS47612.2022.9981976.

Clustering and Informative Path Planning for 3D Gas Distribution Mapping: Algorithms and Performance Evaluation

C. Ercolani; L. Tang; A. A. Humne; A. Martinoli 

IEEE Robotics and Automation Letters. 2022. Vol. 7, num. 2, p. 5310 – 5317. DOI : 10.1109/LRA.2022.3154026.

3D Odor Source Localization Using a Micro Aerial Vehicle: System Design and Performance Evaluation

C. Ercolani; A. Martinoli 

2020. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), Las Vegas, Nevada, US, October 25-29, 2020. p. 6194-6200. DOI : 10.1109/IROS45743.2020.9341501.