{"id":76302,"date":"2018-03-09T23:21:39","date_gmt":"2018-03-09T23:21:39","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/mapeado-3d-con-robots-autonomos-mediante-vision-estereo\/"},"modified":"2018-03-09T23:21:39","modified_gmt":"2018-03-09T23:21:39","slug":"mapeado-3d-con-robots-autonomos-mediante-vision-estereo","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/mapeado-3d-con-robots-autonomos-mediante-vision-estereo\/","title":{"rendered":"Mapeado 3d con robots aut\u00f3nomos mediante visi\u00f3n est\u00e9reo."},"content":{"rendered":"<h2>Tesis doctoral de <strong> Juan  Manuel S\u00e1ez Mart\u00ednez <\/strong><\/h2>\n<p>En este trabajo presentamos una aproximaci\u00f3n al problema de mapeado y localizaci\u00f3n simult\u00e1neos (slam), considerado actualmente como uno de los pilares b\u00e1sicos de la rob\u00f3tica m\u00f3vil. Para ello, recurrimos a la informaci\u00f3n de apariencia y estructura 3d de la escena, obtenida a trav\u00e9s de una c\u00e1mara estereosc\u00f3pica, que representa el \u00fanico sensor del sistema. Este dispositivo ofrece observaciones semi-densas del entorno, con un ruido no isotr\u00f3pico significativo. En primer lugar, presentamos un algoritmo r\u00e1pido y robusto para estimar las acciones del robot en funci\u00f3n de sus observaciones, lo cual nos permite prescindir de un sensor de odometria adicional. El algoritmo realiza la estimaci\u00f3n de cada acci\u00f3n con informaci\u00f3n local, esto es, utilizando \u00fanicamente las observaciones inmediatamente anterior y posterior en la trayectoria. Para minimizar el tiempo de c\u00f3mputo, el c\u00e1lculo se realiza sobre una selecci\u00f3n de los puntos de cada observaci\u00f3n (observaciones reducidas).Ei algoritmo consta de tres fases: emparejamiento inicial, donde se relacionan dos a dos los puntos de las observaciones, utilizando la apariencia y la informaci\u00f3n 3d; refinamiento, donde se filtran los falsos positivos del emparejamiento inicial, utilizando la estructura 3d de las observaciones; y alineamiento, donde se estima la acci\u00f3n realizada en funci\u00f3n del emparejamiento robusto obtenido en la fase anterior.Bajo la suposici\u00f3n de c\u00e1mara y suelo rectos, se consigue acelerar el proceso y minimizar la ambig\u00ed\u00bcedad del mismo. Eliminando esta restricci\u00f3n, y con un cierto incremento tanto en el tiempo de c\u00f3mputo como en el error de la estimaci\u00f3n, se consigue extender la soluci\u00f3n anterior para cubrir el movimiento completo de c\u00e1mara (seis grados de libertad). en segundo lugar, se propone una t\u00e9cnica de rectificaci\u00f3n global. Esta t\u00e9cnica utiliza toda la informaci\u00f3n del mapa para corregir los errores acumulados en la fase de estimaci\u00f3n de acciones. Se tr<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Mapeado 3d con robots aut\u00f3nomos mediante visi\u00f3n est\u00e9reo.<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Mapeado 3d con robots aut\u00f3nomos mediante visi\u00f3n est\u00e9reo. <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Juan  Manuel S\u00e1ez Mart\u00ednez <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Alicante<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 23\/09\/2005<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>Francisco Escolano Ru\u00edz<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: Luis \u00e1lvarez le\u00f3n <\/li>\n<li>Jaime G\u00f3mez Garc\u00eda-bermejo (vocal)<\/li>\n<li>Jos\u00e9 Neira parra (vocal)<\/li>\n<li>Miguel angel Cazorla quevedo (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Juan Manuel S\u00e1ez Mart\u00ednez En este trabajo presentamos una aproximaci\u00f3n al problema de mapeado y localizaci\u00f3n simult\u00e1neos [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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