In our research, we are concerned with sensing the environment using mobile robots. This enables selection of optimal sampling locations in order to produce maximum information about the environment. Selection of sampling locations plays a key role in hospital environments, for example, where humidity and temperature levels or carbon dioxide concentration may require regular monitoring. On the other hand, the accuracy of a regression model depends on the sampling locations, which is significant, for example, in planetary exploration.

In geostatistics optimal spatial sampling strategies search for sensor locations that produce minimal variance in estimates with a restricted number of sensors. Minimal variance is achieved, for example, by minimizing the conditional entropy of unobserved locations, where the environment is modelled using Gaussian processes.

In this paper, we propose an experimental environment for optimal spatial sampling using mobile sensors. Mobility reduces the reliability of sampling locations due to odometer failures, which again reduce the likelihood of the model. We have resolved this problem by building an experimental environment where a ceiling camera vision system provides multi-robot localization in an area measuring approximately 240×160 cm. Preliminary experiments compare optimal spatial sampling for both stationary and nonstationary models using scalar measurements of ambient light and magnetic flux density.