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.
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Acknowledgement
The authors would like to thank SAF€RA for funding the project RASEM, https://www.bam.de/Content/EN/Projects/RASEM/rasem.html and JSPS (KAKENHI Grant Number 22H04952) for funding part of this work.
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Winkler, N.P. et al. (2023). Learning from the Past: Sequential Deep Learning for Gas Distribution Mapping. In: Tardioli, D., Matellán, V., Heredia, G., Silva, M.F., Marques, L. (eds) ROBOT2022: Fifth Iberian Robotics Conference. ROBOT 2022. Lecture Notes in Networks and Systems, vol 590. Springer, Cham. https://doi.org/10.1007/978-3-031-21062-4_15
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DOI: https://doi.org/10.1007/978-3-031-21062-4_15
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