{"id":19,"date":"2023-05-07T22:24:15","date_gmt":"2023-05-07T22:24:15","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/?p=19"},"modified":"2023-05-10T03:09:39","modified_gmt":"2023-05-10T03:09:39","slug":"introduction","status":"publish","type":"post","link":"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/2023\/05\/07\/introduction\/","title":{"rendered":"INTRODUCTION"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">PROBLEM STATEMENT<\/h2>\n\n\n\n<p>Our task is to develop a low-power state estimation algorithm for <a href=\"https:\/\/about.meta.com\/realitylabs\/projectaria\/\" target=\"_blank\" rel=\"noreferrer noopener\"><mark class=\"has-inline-color has-vivid-cyan-blue-color\">Aria glasses<\/mark><\/a><mark style=\"color:#d6eaf7\" class=\"has-inline-color\"> <\/mark>using a history of Inertial Measurement Unit (IMU) measurements combined with a sparse number of RGB images as input. <\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"566\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-14-1024x566.png\" alt=\"\" class=\"wp-image-222\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-14-1024x566.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-14-300x166.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-14-768x425.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-14.png 1244w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">IMUs SUMMARY<\/h2>\n\n\n\n<p>The IMU is a combination of the accelerometer, gyroscope, and magnetometer sensors. The accelerometer sensor measures the linear acceleration while the gyroscope measures the angular velocity. The magnetometer measures the magnetic flux density, which is just a combination of magnetic field strength and direction. <\/p>\n\n\n\n<p>Each of the 3 sensors takes measurements in the 3 axes relative to the device.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"592\" height=\"692\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-5.png\" alt=\"\" class=\"wp-image-94\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-5.png 592w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-5-257x300.png 257w\" sizes=\"auto, (max-width: 592px) 100vw, 592px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">OUR HYPOTHESIS<\/h2>\n\n\n\n<p>Fusing the camera\u2019s relative pose with the inertial sensor measurements can increase the accuracy and robustness of the state estimates when compared with inertial-only methods such as IDOL with just a small increase in power consumption. <\/p>\n\n\n\n<p> <\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><\/h2>\n","protected":false},"excerpt":{"rendered":"<p>PROBLEM STATEMENT Our task is to develop a low-power state estimation algorithm for Aria glasses using a history of Inertial Measurement Unit (IMU) measurements combined with a sparse number of RGB images as input. IMUs SUMMARY The IMU is a combination of the accelerometer, gyroscope, and magnetometer sensors. The accelerometer sensor measures the linear acceleration &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/2023\/05\/07\/introduction\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;INTRODUCTION&#8221;<\/span><\/a><\/p>\n","protected":false},"author":178,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-19","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>INTRODUCTION - Sparse Image and dual IMU localization for AR glasses<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/2023\/05\/07\/introduction\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"INTRODUCTION - Sparse Image and dual IMU localization for AR glasses\" \/>\n<meta property=\"og:description\" content=\"PROBLEM STATEMENT Our task is to develop a low-power state estimation algorithm for Aria glasses using a history of Inertial Measurement Unit (IMU) measurements combined with a sparse number of RGB images as input. 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