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.

翻译：气体源定位（GSL）是自主机器人领域具有广泛应用前景的问题，涵盖从管道泄漏检测到应急响应等场景。本研究提出一种在存在障碍物与湍流的真实室内环境中实现气体源定位的新方法。针对气体源位置与机器人可获取测量值（单点气体浓度及风矢量）之间高度复杂的关系，我们提出一种观测模型——该模型通过将任意候选源位置的气体扩散实时在线模拟结果，与传感器读数构建的气体浓度图进行对比而建立。为将两者便捷且严谨地整合至概率估计框架中，我们引入概率气体命中图概念，该概念通过提供更高层级的抽象来建模气体扩散的时变特性。仿真实验与真实实验的结果均表明，本方法能够有效处理复杂室内环境中的气体源定位问题。