Statistical Gas Distribution Modeling Using Kernel Methods

Author:

Asadi Sahar1,Reggente Matteo1,Stachniss Cyrill2,Plagemann Christian3,Lilienthal Achim J.1

Affiliation:

1. Örebro University, Sweden

2. University of Freiburg, Germany

3. Stanford University, USA

Abstract

Gas distribution models can provide comprehensive information about a large number of gas concentration measurements, highlighting, for example, areas of unusual gas accumulation. They can also help to locate gas sources and to plan where future measurements should be carried out. Current physical modeling methods, however, are computationally expensive and not applicable for real world scenarios with real-time and high resolution demands. This chapter reviews kernel methods that statistically model gas distribution. Gas measurements are treated as random variables, and the gas distribution is predicted at unseen locations either using a kernel density estimation or a kernel regression approach. The resulting statistical models do not make strong assumptions about the functional form of the gas distribution, such as the number or locations of gas sources, for example. The major focus of this chapter is on two-dimensional models that provide estimates for the means and predictive variances of the distribution. Furthermore, three extensions to the presented kernel density estimation algorithm are described, which allow to include wind information, to extend the model to three dimensions, and to reflect time-dependent changes of the random process that generates the gas distribution measurements. All methods are discussed based on experimental validation using real sensor data.

Publisher

IGI Global

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Low-to-High Resolution Path Planner for Robotic Gas Distribution Mapping;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

2. Scalable probabilistic gas distribution mapping using Gaussian belief propagation;2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2022-10-23

3. Integrating SLAM and gas distribution mapping (SLAM-GDM) for real-time gas source localization;Advanced Robotics;2018-09-02

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