ICASSP 2025 SP Grand Challenge: Gas source localization from real-world spatial in-situ concentration and wind measurements.

The scope of this challenge is development of signal processing methods for localizing a gas source using in-situ wind speed and gas concentrations measurements. The methods are designed to advance robotic olfaction and associated autonomous robotic exploration techniques -- highly relevant yet challenging problems in the context of gas source localization, environmental monitoring, and civil security, to name only a few.

Development of the corresponding techniques, however, requires experimental data to benchmark different methodologies and get better understanding of the observed phenomena. Yet accurate, realistic gas observations with robotic platforms remain challenging due to a number of factors, such as variability of the environment (e.g., temperature, wind, or propagation geometry), sensor limitations, and as well as interference from various sources. As a consequence, advanced data analysis and careful experimental designs are essential to address these challenges and achieve reliable gas observations. To address these issues, data have been collected under controlled conditions in the Low-Speed Wind Tunnel (LST), Marknese, Netherlands. The wind tunnel setting allows studying gas propagation under stable wind conditions, thus providing quasi-stationary measurements. By using a synthetic gas source, realized with a commercial fog machine with custom fog fluid, along with a specially constructed sampling device, a high resolution sampling and sensor characterization can be realized. For gas sensing, commercially available and compact sensors like MOX (metal oxide) and PID (photo-ionization) are used; in addition, anemometers are employed to measure wind at the sampling locations. In this way a comprehensive, accurately localized ground truth gas observations are collected. 

The goal of this challenge is to utilize the collected data to benchmark and advance corresponding signal processing methods for gas sensing and olfaction. Specifically, using the collected data, the participants are tasked with solving the following Gas Source Localization (GSL) problem:

Given samples of measured gas concentration values and wind at different spatial locations of the exploration area,  

1. determine the location of the gas source, and
2. provide the corresponding uncertainty estimate, thus quantifying localization precision.

The data set provides sufficient material for developing and testing signal processing tools. Several measurement settings are used: one for training, and one for validation of the developed methods. We expect successful contributions to enhance existing data-driven or model-based techniques, or propose original, novel solutions to this GSL challenge. The proposed methods should be efficient, since robotic measurements are often ``expensive''. This implies that the designed solutions should utilize as few measurements as possible to achieve a precise and accurate source localization. The achieved accuracy and precision of GSL solution versus the number of required samples will thus be utilized as key metric to compare different algorithms.