<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0121-750X</journal-id>
<journal-title><![CDATA[Ingeniería]]></journal-title>
<abbrev-journal-title><![CDATA[ing.]]></abbrev-journal-title>
<issn>0121-750X</issn>
<publisher>
<publisher-name><![CDATA[Universidad Distrital Francisco José de Caldas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0121-750X2021000200284</article-id>
<article-id pub-id-type="doi">10.14483/23448393.16562</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Vision-based Software Tool System for Position Estimation Using a Smartphone]]></article-title>
<article-title xml:lang="es"><![CDATA[Sistema de herramientas software basado en visión para la estimación de posición usando un teléfono inteligente]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Urbano]]></surname>
<given-names><![CDATA[Julio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bacca-Cortes]]></surname>
<given-names><![CDATA[Bladimir]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Buitrago-Molina]]></surname>
<given-names><![CDATA[José]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad del Valle  ]]></institution>
<addr-line><![CDATA[Cali ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad del Valle  ]]></institution>
<addr-line><![CDATA[Cali ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad del Valle  ]]></institution>
<addr-line><![CDATA[Cali ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2021</year>
</pub-date>
<volume>26</volume>
<numero>2</numero>
<fpage>284</fpage>
<lpage>305</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0121-750X2021000200284&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0121-750X2021000200284&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0121-750X2021000200284&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Context:  Current smartphone models have a very interesting set of sensors such as cameras, IMUs, GPS, and environmental variables. This combination of sensors motivates the use of smartphones in scientific and service applications. One of these applications is precision agriculture, specifically drone position estimation using computer vision in GPS-denied environments for remote crop measurements.  Method:  This work presents the development of EVP, a vision-based position estimation system using a modern smartphone and computer vision methods. EVP consists of two software applications: an Android app (mobile station) running on a smartphone capable of controlling the drone&#8217;s flight, acquiring images, and transmitting them through a wireless network; and another application (base station) running on a Linux-based computer capable of receiving the images, processing them and running the position estimation algorithms using the acquired images. In this work, the mobile station is placed in a quadcopter. Using EVP, users can configure the mobile and base station software, execute the vision-based position estimation method, observe position graph results on the base station, and store sensor data in a database.  Results:  EVP was tested in three field tests: an indoor environment, an open field flight, and a field test over the Engineering Department&#8217;s square at Universidad del Valle. The root mean square errors obtained in XY were 0,166 m, 2,8 m, and 1,4 m, respectively, and they were compared against the GPS-RTK measurements.  Conclusions:  As a result, a vision-based position estimation system called EVP was developed and tested in realworld experiments. This system can be used in GPS-denied environments to perform tasks such as 3D mapping, pick-up and delivery of goods, object tracking, among others.  Acknowledgements:  This work was partially funded by the research project &#8220;Autonomous Aerial System to Map the Nitrogen Contents in Crops using Micro-Spectral Sensors&#8221;, contract CI2961 of Universidad del Valle.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Contexto:  Los actuales modelos de teléfonos inteligentes tienen un conjunto muy interesante de sensores como cámaras, IMUs, GPS y variables ambientales. Esta combinación de sensores motiva el uso de los teléfonos inteligentes en aplicaciones científicas y de servicio. Una de estas aplicaciones es la agricultura de precisión, específicamente la estimación de posición de un dron usando visión por computador para realizar mediciones remotas en cultivos.  Método:  Este trabajo presenta el desarrollo de EVP, un sistema de estimación de posición basado en visión usando un teléfono inteligente moderno y métodos de visión por computador. EVP consiste en dos aplicaciones software, una app de Android (estación móvil) ejecutándose en un teléfono inteligente capaz de controlar el vuelo de un dron, capturar imágenes, y transmitirlas a través de una red inalámbrica; y otra aplicación (estación base) ejecutándose en un PC Linux capaz de recibir imágenes, procesarlas y ejecutar los algoritmos de estimación de posición usando las imágenes adquiridas. En este trabajo, la estación móvil está ubicada en un cuadracóptero. Usando EVP, los usuarios pueden configurar el software de la estación móvil y base, ejecutar el método de estimación de posición basado en visión, observar resultados gráficos de la posición en la estación base y almacenar los datos adquiridos en una base de datos.  Resultados:  EVP fue probada en tres pruebas de campo: en ambientes de interiores, un vuelo en campo abierto, y sobre la plazoleta de la Facultad de Ingeniería de la Universidad del Valle. Los errores cuadráticos medios en XY fueron 0,166 m, 2,8 m y 1,4 m respectivamente y comparados con las medidas de GPS-RTK.  Conclusiones:  Como resultado, un sistema de estimación de posición basado en visión llamado EVP fue desarrollado y probado en experimentos reales. Este sistema puede ser usado en ambientes sin señal de GPS para realizar tareas como mapeo 3D, recogida y entrega de materiales, seguimiento de objetivos, entre otros.  Agradecimientos:  Este trabajo fue parcialmente financiado por el proyecto de investigación &#8220;Sistema Aéreo Autónomo para Mapear el Contenido de Nitrógeno en Cultivos usando Sensores Microespectrales&#8221;, contrato CI2961 de la Universidad del Valle.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Smartphone]]></kwd>
<kwd lng="en"><![CDATA[position estimation]]></kwd>
<kwd lng="en"><![CDATA[computer vision.]]></kwd>
<kwd lng="es"><![CDATA[Teléfono inteligente]]></kwd>
<kwd lng="es"><![CDATA[estimación de posición]]></kwd>
<kwd lng="es"><![CDATA[visión por computador.]]></kwd>
</kwd-group>
</article-meta>
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