Gas source localization (GSL) with an autonomous robot is a problem with many prospective applications, from finding pipe leaks to emergency-response scenarios. In this work we present a new method to perform GSL in realistic indoor environments, featuring obstacles and turbulent flow. Given the highly complex relationship between the source position and the measurements available to the robot (the single-point gas concentration, and the wind vector) we propose an observation model that derives from contrasting the online, real-time simulation of the gas dispersion from any candidate source localization against a gas concentration map built from sensor readings. To account for a convenient and grounded integration of both into a probabilistic estimation framework, we introduce the concept of probabilistic gas-hit maps, which provide a higher level of abstraction to model the time-dependent nature of gas dispersion. Results from both simulated and real experiments show the capabilities of our current proposal to deal with source localization in complex indoor environments. To the best of our knowledge, this is the first work in olfactory robotics that doesn't make simplistic assumptions about environmental conditions like operating in open spaces and/or having an unrealistic laminar flow wind.

翻译：自主机器人的气体源定位（GSL）问题具有众多潜在应用场景，涵盖管道泄漏检测到应急响应等领域。本文提出一种在具有障碍物与湍流环境的真实室内场景中执行GSL的新方法。针对源位置与机器人可获取的观测数据（单点气体浓度与风矢量）之间高度复杂的映射关系，我们提出一种观测模型：通过对比基于任意候选源位置进行实时在线气体扩散模拟的结果与传感器读数构建的气体浓度地图，实现两者差异量化。为将两者便捷且严谨地融入概率估计框架，我们引入概率性气体击中地图概念，该抽象建模方法能更高效地表征气体扩散的时间依赖性。仿真与实物实验结果表明，本方案具备在复杂室内环境中实现源定位的能力。据我们所知，这是嗅觉机器人领域首个未对开放式空间或非真实层流风等环境条件作出简化假设的研究。