Time-variant gas distribution mapping with obstacle information

Abstract

This paper addresses the problem of estimating the spatial distribution of volatile substances using a mobile robot equipped with an electronic nose (e-nose). Our work contributes an effective solution to two important problems that have been disregarded so far: First, obstacles in the environment (walls, furniture, ...) do affect the gas spatial distribution. Second, when combining odor measurements taken at different instants of time, their ’ages’ must be taken into account to model the ephemeral nature of gas distributions. In order to incorporate these two characteristics into the mapping process we propose modeling the spatial distribution of gases as a Gaussian Markov random field (GMRF). This mathematical framework allows us to consider both: (i) the vanishing information of gas readings by means of a time-increasing uncertainty in sensor measurements, and (ii) the influence of objects in the environment by means of correlations among the different areas. Experimental validation is provided with both, simulated and real-world datasets, demonstrating the out-performance of our method when compared to previous standard techniques in gas mapping.