TY - GEN
T1 - A statistical approach to gas distribution modelling with mobile robots - The Kernel DM+V algorithm
AU - Lilienthal, Achim J.
AU - Reggente, Matteo
AU - Trinca, Marco
AU - Blanco, Jose Luis
AU - Gonzalez, Javier
PY - 2009/12/11
Y1 - 2009/12/11
N2 - 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 evaluation, which has always been a critical issue for gas distribution modelling. Estimating the predictive variance also provides the means to learn meta parameters and to suggest new measurement locations based on the current model. We derive the Kernel DM+V algorithm and present a method for learning the hyper-parameters. Based on real world data collected with a mobile robot we demonstrate the consistency of the obtained maps and present a quantitative comparison, in terms of the data likelihood of unseen samples, with an alternative approach that estimates the predictive variance.
AB - 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 evaluation, which has always been a critical issue for gas distribution modelling. Estimating the predictive variance also provides the means to learn meta parameters and to suggest new measurement locations based on the current model. We derive the Kernel DM+V algorithm and present a method for learning the hyper-parameters. Based on real world data collected with a mobile robot we demonstrate the consistency of the obtained maps and present a quantitative comparison, in terms of the data likelihood of unseen samples, with an alternative approach that estimates the predictive variance.
UR - https://www.scopus.com/pages/publications/76249127720
U2 - 10.1109/IROS.2009.5354304
DO - 10.1109/IROS.2009.5354304
M3 - Conference contribution
AN - SCOPUS:76249127720
SN - 9781424438044
T3 - 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
SP - 570
EP - 576
BT - 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
T2 - 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
Y2 - 11 October 2009 through 15 October 2009
ER -