A statistical approach to gas distribution modelling with mobile robots - The Kernel DM+V algorithm

— Gas distribution modelling constitutes an ideal application area for mobile robots, which – as intelligent mobile gas sensors – offer several advantages compared to stationary sensor networks. In this paper we propose the Kernel DM+V algorithm to learn a statistical 2-d gas distribution model from a sequence of localized gas sensor measurements. The algorithm does not make strong assumptions about the sensing locations and can thus be applied on a mobile robot that is not primarily used for gas distribution monitoring, and also in the case of stationary measurements. Kernel DM+V treats distribution modelling as a density estimation problem. In contrast to most previous approaches, it models the variance in addition to the distribution mean. Estimating the predictive variance entails a significant improvement for gas distribution modelling since it allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluat...

• Type: Conference
• Year: 2009
• Where: IROS
• Authors: Achim J. Lilienthal, Matteo Reggente, Marco Trincavelli, Jose-Luis Blanco, Javier Gonzalez
• Keywords: Gas Distribution, IROS 2009, Mobile Robot, Predictive Variance, Robotics