{"id":28,"date":"2023-05-07T23:16:19","date_gmt":"2023-05-07T23:16:19","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/?p=28"},"modified":"2023-05-10T02:47:15","modified_gmt":"2023-05-10T02:47:15","slug":"step-wise-methodology","status":"publish","type":"post","link":"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/2023\/05\/07\/step-wise-methodology\/","title":{"rendered":"OUR CONTRIBUTIONS"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">VISUAL ODOMETRY MODULE<\/h2>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/Picture1-1024x521.png\" alt=\"\" class=\"wp-image-29\" width=\"797\" height=\"405\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/Picture1-1024x521.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/Picture1-300x153.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/Picture1-768x391.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/Picture1-1536x781.png 1536w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/Picture1-2048x1042.png 2048w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<p>We implement <a href=\"https:\/\/arxiv.org\/pdf\/1505.07427v4.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark class=\"has-inline-color has-vivid-cyan-blue-color\">PoseNet<\/mark><\/a>  with <a href=\"https:\/\/arxiv.org\/pdf\/1409.4842.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark class=\"has-inline-color has-vivid-cyan-blue-color\">InceptionNet<\/mark><\/a> backbone with appropriate changes to the final fully-connected layers to regress the 7D pose i.e. 3D position (x, y, z) and 4D quaternion. <\/p>\n\n\n\n<p>We train this model on the dataset collected in the basement of Smith Hall using Aria Glasses. For establishing our baseline, we have also tested our model on the King&#8217;s College Dataset in order to compare it against <a href=\"https:\/\/arxiv.org\/pdf\/1505.07427v4.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark class=\"has-inline-color has-vivid-cyan-blue-color\">PoseNet<\/mark><\/a>[1] implementation. <\/p>\n\n\n\n<p>Using the RGB camera ensures that the predicted pose is quite accurate with the only downside of a higher power consumption. <\/p>\n\n\n\n<h2 class=\"wp-block-heading\">INERTIAL ODOMETRY MODULE<\/h2>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-3-1024x593.png\" alt=\"\" class=\"wp-image-61\" width=\"757\" height=\"438\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-3-1024x593.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-3-300x174.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-3-768x444.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-3-1536x889.png 1536w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-3-2048x1185.png 2048w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<p>We use IDOL: <a href=\"https:\/\/arxiv.org\/pdf\/2102.04024.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark class=\"has-inline-color has-vivid-cyan-blue-color\">Inertial Deep Orientation-Estimation and Localization<\/mark><\/a>[2] architecture as our baseline. We have trained this model on IDOL dataset and also on our dataset collected using Aria glasses. <\/p>\n\n\n\n<p>This model aims to regress the pose using IMU readings.<\/p>\n\n\n\n<p>These sensors can operate using very low power consumption but they accumulate drift quickly over time. Hence, they are accurate only for a short duration. <\/p>\n\n\n\n<h2 class=\"wp-block-heading\">COMBINED LOW-POWER VISUAL-INERTIAL ODOMETRY MODULE<\/h2>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"446\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-12-1024x446.png\" alt=\"\" class=\"wp-image-207\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-12-1024x446.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-12-300x131.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-12-768x335.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-12-1536x670.png 1536w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-12.png 1780w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><figcaption class=\"wp-element-caption\">Architecture combining the VO and IO modules<\/figcaption><\/figure>\n<\/div>\n\n\n<p>In order to find a balance between the pros and cons of the visual odometry system and the inertial odometry system, we combine the inertial odometry model with the visual odometry model. We use the visual odometry prediction after every k<sup>th <\/sup>timestep such that we can reset the inertial odometry system which has collected drift till this timestep. <\/p>\n\n\n\n<p>We try to find the best possible &#8216;k&#8217; value which strikes the balance between power consumption i.e. frequency of RGB frames used v\/s the accuracy of the overall predictions. <\/p>\n\n\n\n<h2 class=\"wp-block-heading\">OVERVIEW OF THE ABOVE METHODS<\/h2>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-4-1024x372.png\" alt=\"\" class=\"wp-image-74\" width=\"890\" height=\"323\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-4-1024x372.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-4-300x109.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-4-768x279.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-4-1536x558.png 1536w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-4.png 1916w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">REFERENCES<\/h2>\n\n\n\n<p>[1] Kendall, Alex, et al. &#8220;PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization&#8221;, <em>IEEE International Conference on Computer Vision (ICCV), 2015<\/em><\/p>\n\n\n\n<p>[2] Sun, Scott, et al. &#8220;<a href=\"Inertial Deep Orientation-Estimation and Localization\">IDOL: Inertial Deep Orientation-Estimation and Localization<\/a>&#8220;, <em>AAAI Conference on Artificial Intelligence, 2021<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>VISUAL ODOMETRY MODULE We implement PoseNet with InceptionNet backbone with appropriate changes to the final fully-connected layers to regress the 7D pose i.e. 3D position (x, y, z) and 4D quaternion. We train this model on the dataset collected in the basement of Smith Hall using Aria Glasses. For establishing our baseline, we have also &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/2023\/05\/07\/step-wise-methodology\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;OUR CONTRIBUTIONS&#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":[4],"tags":[],"class_list":["post-28","post","type-post","status-publish","format-standard","hentry","category-method"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>OUR CONTRIBUTIONS - 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\/step-wise-methodology\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"OUR CONTRIBUTIONS - Sparse Image and dual IMU localization for AR glasses\" \/>\n<meta property=\"og:description\" content=\"VISUAL ODOMETRY MODULE We implement PoseNet with InceptionNet backbone with appropriate changes to the final fully-connected layers to regress the 7D pose i.e. 3D position (x, y, z) and 4D quaternion. We train this model on the dataset collected in the basement of Smith Hall using Aria Glasses. For establishing our baseline, we have also &hellip; Continue reading &quot;OUR CONTRIBUTIONS&quot;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/2023\/05\/07\/step-wise-methodology\/\" \/>\n<meta property=\"og:site_name\" content=\"Sparse Image and dual IMU localization for AR glasses\" \/>\n<meta property=\"article:published_time\" content=\"2023-05-07T23:16:19+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-05-10T02:47:15+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/Picture1-1024x521.png\" \/>\n<meta name=\"author\" content=\"dishanil\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"dishanil\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team14\\\/2023\\\/05\\\/07\\\/step-wise-methodology\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team14\\\/2023\\\/05\\\/07\\\/step-wise-methodology\\\/\"},\"author\":{\"name\":\"dishanil\",\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team14\\\/#\\\/schema\\\/person\\\/bb79ff57dfca5bf74ee5d99394fc2d2a\"},\"headline\":\"OUR CONTRIBUTIONS\",\"datePublished\":\"2023-05-07T23:16:19+00:00\",\"dateModified\":\"2023-05-10T02:47:15+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team14\\\/2023\\\/05\\\/07\\\/step-wise-methodology\\\/\"},\"wordCount\":305,\"image\":{\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team14\\\/2023\\\/05\\\/07\\\/step-wise-methodology\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team14\\\/wp-content\\\/uploads\\\/sites\\\/91\\\/2023\\\/05\\\/Picture1-1024x521.png\",\"articleSection\":[\"Method\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team14\\\/2023\\\/05\\\/07\\\/step-wise-methodology\\\/\",\"url\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team14\\\/2023\\\/05\\\/07\\\/step-wise-methodology\\\/\",\"name\":\"OUR CONTRIBUTIONS - 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