A Grid-based Sensor Floor Platform for Robot Localization using Machine Learning

Gouda, Anas; Heinrich, Danny; Hünnefeld, Mirco; Priyanta, Irfan Fachrudin; Reining, Christopher; Roidl, Moritz

doi:10.1109/i2mtc53148.2023.10176013

Cited by 1 publication

(1 citation statement)

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“…al. [18] used Inertial Measurement Unit (IMU) with Convolutional Neural Networks (CNN) to estimate robot locations in evenly distributed sensor networks. Also Yuan et.…”

Section: Introductionmentioning

confidence: 99%

ConvLSTM-Based Vehicle Detection and Localization in Seismic Sensor Networks

Köse,

Hocaoğlu

2023

IEEE Access

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Localization of moving military vehicles plays a vital role for border security and safeguarding high-security facilities. Commonly applied range-based localization techniques such as time of arrival, time difference of arrival, angle of arrival, and received signal strength rely on known transmitters. However, when seismic sensor networks are used for localization of moving targets, where moving targets can be treated as unknown transmitters. In this work, we consider a scenario where only receivers are deployed to perceive seismic signals transmitted by the moving military vehicles with unknown locations. Consequently, conventional closed-form equations for distance-based trilateration are not applicable. To address this challenge, we present a novel approach for accurate localization. Our method involves clustering closely deployed sensor nodes to effectively fuse their information to estimate the positions of the moving military vehicles. We leverage multiple-input convolutional neural networks, utilizing one input to represent the short-time discrete Fourier transform of signals from each node, and another input to encode the relative locations of sensors within clusters. Through extensive experimentation, we demonstrate that our proposed method significantly reduces localization errors when compared to existing distributed regression methods.

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“…al. [18] used Inertial Measurement Unit (IMU) with Convolutional Neural Networks (CNN) to estimate robot locations in evenly distributed sensor networks. Also Yuan et.…”

Section: Introductionmentioning

confidence: 99%

ConvLSTM-Based Vehicle Detection and Localization in Seismic Sensor Networks

Köse,

Hocaoğlu

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

A Grid-based Sensor Floor Platform for Robot Localization using Machine Learning

Cited by 1 publication

References 10 publications

ConvLSTM-Based Vehicle Detection and Localization in Seismic Sensor Networks

ConvLSTM-Based Vehicle Detection and Localization in Seismic Sensor Networks

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