Abstract This work addresses 2D gas and wind distribution mapping with a mobile robot for real-time applications. Our proposal seeks to estimate how gases released in the environment are distributed from a set of sparse and uncertain gas-concentration and wind-flow measurements; such that by exploiting the high correlation between these two magnitudes we may extrapolate their value for unexplored areas. Furthermore, because the air currents are completely conditioned by the environment, we assume a priori knowledge of static elements such as walls and obstacles when estimating both distribution maps. In particular, this joint estimation problem is modeled as a multivariate Gaussian Markov random field (GMRF), combining gas and wind observations under a common maximum a posteriori estimation problem. It considers two lattices of cells (a scalar gas-concentration field and a wind vector field) which are correlated following the physical laws of gas dispersal and fluid dynamics. Finally, we report various experiments in which our proposal is compared to other stochastic gas and gas-wind modeling methods under simulation, to evaluate their performance against a computer fluid-dynamics generated ground-truth, as well as under real and uncontrolled conditions.

中文翻译：

---

用于快速响应应用的气体和风图的联合估计

摘要 这项工作解决了使用移动机器人进行实时应用的二维气体和风分布图。我们的提议旨在通过一组稀疏且不确定的气体浓度和风流测量来估计环境中释放的气体是如何分布的；这样，通过利用这两个量级之间的高度相关性，我们可以推断它们对未勘探区域的价值。此外，由于气流完全受环境影响，因此在估计两个分布图时，我们假设对静态元素（例如墙壁和障碍物）有先验知识。特别是，这个联合估计问题被建模为多元高斯马尔可夫随机场 (GMRF)，在一个共同的最大后验估计问题下结合了气体和风的观测。它考虑了遵循气体扩散和流体动力学物理定律的两个单元格（标量气体浓度场和风矢量场）。最后，我们报告了各种实验，在这些实验中，我们的提议与模拟下的其他随机气体和气风建模方法进行了比较，以根据计算机流体动力学生成的地面实况以及在真实和不受控制的条件下评估它们的性能。