Mapping multiple gas/odor sources in an uncontrolled indoor environment using a Bayesian occupancy grid mapping based method

  • Authors:

  • Gabriele Ferri;Michael V. Jakuba;Alessio Mondini;Virgilio Mattoli;Barbara Mazzolai;Dana R. Yoerger;Paolo Dario

  • Affiliations:

  • The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, 56025 Pontedera (Pisa), Italy;Australian Center for Field Robotics, University of Sydney, Rose Street Bldg, J04 Sydney, NSW, 2006, Australia;The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, 56025 Pontedera (Pisa), Italy;Center of Micro-BioRobotics@SSSA, Italian Institute of Technology, Viale Rinaldo Piaggio 34, 56025 Pontedera (Pisa), Italy;Center of Micro-BioRobotics@SSSA, Italian Institute of Technology, Viale Rinaldo Piaggio 34, 56025 Pontedera (Pisa), Italy;Deep Submergence Laboratory, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA;The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, 56025 Pontedera (Pisa), Italy

  • Venue:
  • Robotics and Autonomous Systems
  • Year:
  • 2011

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Abstract

In this paper we address the problem of autonomously localizing multiple gas/odor sources in an indoor environment without a strong airflow. To do this, a robot iteratively creates an occupancy grid map. The produced map shows the probability each discrete cell contains a source. Our approach is based on a recent adaptation (Jakuba, 2007) [16] to traditional Bayesian occupancy grid mapping for chemical source localization problems. The approach is less sensitive, in the considered scenario, to the choice of the algorithm parameters. We present experimental results with a robot in an indoor uncontrolled corridor in the presence of different ejecting sources proving the method is able to build reliable maps quickly (5.5 minutes in a 6 mx2.1 m area) and in real time.