Statistical Gas Distribution Modelling for Mobile Robot
Applications
av
Matteo Reggente
Akademisk avhandling
Avhandling för teknologi doktorsexamen i datavetenskap, som kommer att försvaras offentligt
Fredag den 26 September 2014 kl. 09.00,
Hörsal etc, Örebro universitet/Universitetssjukhuset Örebro Opponent: Prof. Krishna Persaud
University of Manchester Manchester, United Kingdom
Örebro universitet
Institutionen för naturvetenskap och teknik 701 82 Örebro
Abstract
Matteo Reggente (2014): Statistical Gas Distribution Modelling for Mobile
Robot Applications. Örebro Studies in Technology 62.
In this dissertation, we present and evaluate algorithms for statistical gas distri-bution modelling in mobile robot applications. We derive a representation of the gas distribution in natural environments using gas measurements collected with mobile robots. The algorithms fuse different sensors readings (gas, wind and loca-tion) to create 2D or 3D maps.
Throughout this thesis, the Kernel DM+V algorithm plays a central role in modelling the gas distribution. The key idea is the spatial extrapolation of the gas measurement using a Gaussian kernel. The algorithm produces four maps: the weight map shows the density of the measurements; the confidence map shows areas in which the model is considered being trustful; the mean map represents the modelled gas distribution; the variance map represents the spatial structure of the variance of the mean estimate.
The Kernel DM+V/W algorithm incorporates wind measurements in the com-putation of the models by modifying the shape of the Gaussian kernel according to the local wind direction and magnitude.
The Kernel 3D-DM+V/W algorithm extends the previous algorithm to the third dimension using a tri-variate Gaussian kernel.
Ground-truth evaluation is a critical issue for gas distribution modelling with mobile platforms. We propose two methods to evaluate gas distribution models. Firstly, we create a ground-truth gas distribution using a simulation environment, and we compare the models with this ground-truth gas distribution. Secondly, considering that a good model should explain the measurements and accurately predicts new ones, we evaluate the models according to their ability in inferring unseen gas concentrations.
We evaluate the algorithms carrying out experiments in different environments. We start with a simulated environment and we end in urban applications, in which we integrated gas sensors on robots designed for urban hygiene. We found that typically the models that comprise wind information outperform the models that do not include the wind data.
Keywords: statistical modelling; gas distribution mapping; mobile robots; gas sensors; kernel density estimation; Gaussian kernel.
Matteo Reggente, School of Science and Technology
