{"id":1212,"date":"2014-11-11T17:20:28","date_gmt":"2014-11-11T17:20:28","guid":{"rendered":"http:\/\/www.rtc.us.es\/?p=1212"},"modified":"2017-07-08T18:25:42","modified_gmt":"2017-07-08T18:25:42","slug":"mobile-robot-motion-planning-based-on-cloud-computing-stereo-vision-processing","status":"publish","type":"post","link":"https:\/\/grupo.us.es\/rtclab\/mobile-robot-motion-planning-based-on-cloud-computing-stereo-vision-processing\/","title":{"rendered":"Mobile robot motion planning based on Cloud Computing stereo vision processing"},"content":{"rendered":"<p style=\"text-align: justify;\"><strong>Abstract:\u00a0<\/strong>Nowadays, the limitations of robot embedded hardware (which cannot be upgraded easily) make difficult to perform computationally complex tasks such as those of high level artificial vision. However, instead of disposing these \u201coutdated\u201d embedded systems, Cloud technologies for computation offloading can be used. In this paper we present and analyze an example of computation offloading in the context of artifical vision: point cloud extraction for stereo images. A prototype prepared for exploiting the cloud&#8217;s unique capabilities (such as elasticity) has been developed, and the inherent issues that appears are explained and addressed<\/p>\n<p><a href=\"http:\/\/www.rtc.us.es\/wp-content\/uploads\/2014\/11\/clousCom2.jpg\"><img loading=\"lazy\" class=\"wp-image-1229 aligncenter\" src=\"http:\/\/www.rtc.us.es\/wp-content\/uploads\/2014\/11\/clousCom2.jpg\" alt=\"clousCom2\" width=\"561\" height=\"323\" srcset=\"https:\/\/grupo.us.es\/rtclab\/wp-content\/uploads\/2014\/11\/clousCom2.jpg 1298w, https:\/\/grupo.us.es\/rtclab\/wp-content\/uploads\/2014\/11\/clousCom2-300x173.jpg 300w, https:\/\/grupo.us.es\/rtclab\/wp-content\/uploads\/2014\/11\/clousCom2-1024x590.jpg 1024w\" sizes=\"(max-width: 561px) 100vw, 561px\" \/><\/a><\/p>\n<p style=\"text-align: justify;\"><strong>Full text:<\/strong>\u00a0 <a href=\"http:\/\/ieeexplore.ieee.org\/stamp\/stamp.jsp?tp=&amp;arnumber=6840196\">http:\/\/ieeexplore.ieee.org\/stamp\/stamp.jsp?tp=&amp;arnumber=6840196<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Abstract:\u00a0Nowadays, the limitations of robot embedded hardware (which cannot be upgraded easily) make difficult to perform computationally complex tasks such as those of high level artificial vision. However, instead of [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1229,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[25,6,7,11,27],"tags":[],"_links":{"self":[{"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/posts\/1212"}],"collection":[{"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/comments?post=1212"}],"version-history":[{"count":7,"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/posts\/1212\/revisions"}],"predecessor-version":[{"id":1705,"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/posts\/1212\/revisions\/1705"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/media\/1229"}],"wp:attachment":[{"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/media?parent=1212"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/categories?post=1212"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/grupo.us.es\/rtclab\/wp-json\/wp\/v2\/tags?post=1212"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}