@inproceedings{3294987e6862487e976d4a2720aeacea,
title = "Learning from the Past: Sequential Deep Learning for Gas Distribution Mapping",
abstract = "To better understand the dynamics in hazardous environments, gas distribution mapping aims to map the gas concentration levels of a specified area precisely. Sampling is typically carried out in a spatially sparse manner, either with a mobile robot or a sensor network and concentration values between known data points have to be interpolated. In this paper, we investigate sequential deep learning models that are able to map the gas distribution based on a multiple time step input from a sensor network. We propose a novel hybrid convolutional LSTM - transpose convolutional structure that we train with synthetic gas distribution data. Our results show that learning the spatial and temporal correlation of gas plume patterns outperforms a non-sequential neural network model.",
keywords = "Convolutional LSTM, Gas distribution mapping, Sequential learning, Spatial interpolation",
author = "Winkler, {Nicolas P.} and Oleksandr Kotlyar and Erik Schaffernicht and Han Fan and Haruka Matsukura and Hiroshi Ishida and Neumann, {Patrick P.} and Lilienthal, {Achim J.}",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 5th Iberian Robotics Conference, ROBOT 2022 ; Conference date: 23-11-2022 Through 25-11-2022",
year = "2023",
doi = "10.1007/978-3-031-21062-4_15",
language = "English",
isbn = "9783031210617",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "178--188",
editor = "Danilo Tardioli and Vicente Matell{\'a}n and Guillermo Heredia and Silva, {Manuel F.} and Lino Marques",
booktitle = "ROBOT 2022",
}