Localizing multiple gas/odor sources in an indoor environment using bayesian occupancy grid mapping

Abstract

This paper addresses the problem of autonomous localization of multiple gas or odor sources in an indoor environment with no strong airflow. In our approach, a robot iteratively builds an occupancy grid map from successive measurements of odor concentration. The resulting map shows the probability of each discrete cell in the map containing an active plume source. Our method is based on a recent adaptation of Bayesian occupancy grid mapping (OGM) to the chemical plume source localization problem. We present experimental results that demonstrate the utility of the approach.