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：利用自主机器人进行气体源定位（GSL）是一个具有广泛应用前景的问题，从管道泄漏检测到应急响应场景均有涉及。在本研究中，我们提出了一种在具有障碍物和湍流的真实室内环境中执行GSL的新方法。鉴于源位置与机器人可用测量数据（单点气体浓度和风矢量）之间高度复杂的关系，我们提出了一种观测模型，该模型通过对比任意候选源位置的气体在线实时扩散模拟与基于传感器读数构建的气体浓度图而推导得出。为实现两者在概率估计框架中的便捷且基于实际数据的集成，我们引入了概率气体命中图的概念，该概念为建模气体扩散的时变特性提供了更高层次的抽象。仿真与真实实验的结果均表明，我们当前提出的方法能够有效处理复杂室内环境中的源定位问题。