{"id":36140,"date":"1998-01-01T00:00:00","date_gmt":"1998-01-01T00:00:00","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/mobile-robot-localization-and-map-building-a-multisensor-fusion-approach\/"},"modified":"1998-01-01T00:00:00","modified_gmt":"1998-01-01T00:00:00","slug":"mobile-robot-localization-and-map-building-a-multisensor-fusion-approach","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/mobile-robot-localization-and-map-building-a-multisensor-fusion-approach\/","title":{"rendered":"Mobile robot localization and map building: a multisensor fusion approach."},"content":{"rendered":"<h2>Tesis doctoral de <strong>  Castellanos Gomez Jos\u00e9 Angel <\/strong><\/h2>\n<p>El presente trabajo describe nuestra reciente investigaci\u00f3n en los campos de localizaci\u00f3n y construcci\u00f3n de mapas para un robot m\u00f3vil que navega en un entorno de interior construido por el hombre (e.G. Una oficina, un almac\u00e9n, etc). El robot m\u00f3vil est\u00e1 equipado con un sistema multisensorial formado por un sensor de proximetr\u00eda l\u00e1ser y un sistema de visi\u00f3n monocular.  se considera una aproximaci\u00f3n basada en caracter\u00edsticas geom\u00e9tricas, descritas seg\u00fan el modelo perturbacional con simetr\u00edas, mediante una referencia asociada alineada con las simetr\u00edas de la caracter\u00edstica geom\u00e9trica y expresada mediante una transformaci\u00f3n respecto de una referencia base. La incertidumbre de localizaci\u00f3n de las caracter\u00edsticas se ha representado, localmente, mediante una distribuci\u00f3n de ruido blanco gaussiano.  inicialmente se obtiene una representaci\u00f3n del entorno basada en segmentos a partir de la informaci\u00f3n captada por el sensor de proximetr\u00eda l\u00e1ser. Posteriormente se considera la informaci\u00f3n redundante proporcionada por el sensor de visi\u00f3n monocular para detectar caracter\u00edsticas de alto nivel, que son mucho m\u00e1s robustas y fiables, y que en nuestro trabajo se obtienen durante las primeras etapas del procesamiento.  el trabajo afronta el problema del c\u00e1lculo de una estimaci\u00f3n de localizaci\u00f3n para el robot m\u00f3vil cuando no se dispone de ninguna estimaci\u00f3n inicial, es decir, el robot est\u00e1 perdido en el entorno de navegaci\u00f3n. El problema se resuelve mediante un emparejamiento de las observaciones del robot con un mapa del entorno conocido a priori. Se comparan dos algoritmos de emparejamiento basados en un \u00e1rbol de interpretaciones que se recorre eficientemente mediante el uso de restricciones geom\u00e9tricas. Se pone de manifiesto la importancia de seleccionar las observaciones m\u00e1s relevantes durante las primeras etapas del emparejamiento as\u00ed como la importancia de obtener un pronta estimaci\u00f3n de localizaci\u00f3n<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Mobile robot localization and map building: a multisensor fusion approach.<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Mobile robot localization and map building: a multisensor fusion approach. <\/li>\n<li><strong>Autor:<\/strong>\u00a0  Castellanos Gomez Jos\u00e9 Angel <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Zaragoza<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 01\/01\/1998<\/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> Tardos Solano Juan  Domingo<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: Manuel Silva Suarez <\/li>\n<li>Angel Salichs Miguel (vocal)<\/li>\n<li>Gunther Schmidt (vocal)<\/li>\n<li>Javier Gonzalez Jimenez (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Castellanos Gomez Jos\u00e9 Angel El presente trabajo describe nuestra reciente investigaci\u00f3n en los campos de localizaci\u00f3n y [&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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