There are many potential applications for an autonomous robotic agent capable of sensing gases in the environment, from locating leaks in pipes to monitoring air quality. However, the current state of the art in the field of robotic olfaction is not mature enough for most real-world applications. Due to the complexity of gas dispersion phenomena and the limitations of sensors, a great deal of research into the development of techniques and algorithms remains necessary. A very important part of this research is thorough experimentation, but carrying out robotic olfaction experiments is far from trivial. Real world experiments are usually limited to very simplified, wind-tunnel-like environments, as it is impossible to closely monitor or control the airflow in more complex scenarios. For this reason, simulation with CFD offers the most plausible alternative, allowing researchers to study the behavior of their algorithms in more challenging and complex situations. This work presents a CFD-based gas dispersion dataset composed of 120 cases generated under variable environmental conditions, taking place in 30 realistic and detailed models of real houses. All the data is made available in multiple formats, and is directly accessible through ROS, to permit easy integration with other robotic tools.
