Probabilistic Estimation Of The Gas Source Location In Indoor Environments By Combining Gas And Wind Observations

This work deals with the problem of gas source localization by a mobile robot with gas and wind sensing capabilities. Particularly, we address the problem for the case of indoor environments where the presence of obstacles and the complex structure provoke the chaotic dispersion of the gases. Under these challenging conditions where traditional approaches based on mathematical modeling of the plume cannot be applied, we propose the use of numerical methods to solve the gas dispersion and its exploitation in a probabilistic formulation to estimate the likelihood of the gas source location from a set of sparse observations. We validate our approach with a simulated set of experiments in an office-like environment composed of multiple connected rooms. Two search strategies are compared (active and passive) demonstrating the suitability of our approach to infer the location of the source even when the robot is not actively searching for it.