Online estimation of 2D wind maps for olfactory robots

This work introduces a novel solution to approximate in real time the 2D wind flow present in a geometrically known environment. It is grounded on the probabilistic framework provided by a Markov random field and enables the estimation of the most probable wind field from a set of noisy observations, for the case of incompressible and steady wind flow. Our method delivers reasonably precise results without falling into common unrealistic assumptions like homogeneous wind flow, absence of obstacles, etc., and performs very efficiently (less than 0.5 seconds for an environment represented with a 100×100 cell grid). This approach is then quite suitable for applications that require real-time estimation of the wind flow, as for example, the localization of gas sources, prediction of the gas dispersion, or the mapping of the gas distribution of different chemicals released in a given scenario.