{"id":255,"date":"2023-12-17T11:09:48","date_gmt":"2023-12-17T11:09:48","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/?p=255"},"modified":"2023-12-17T11:42:46","modified_gmt":"2023-12-17T11:42:46","slug":"fall-23-results","status":"publish","type":"post","link":"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/2023\/12\/17\/fall-23-results\/","title":{"rendered":"Fall &#8217;23 progress"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Visual odometry<\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"164\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.32.54-PM-1024x164.png\" alt=\"\" class=\"wp-image-260\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.32.54-PM-1024x164.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.32.54-PM-300x48.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.32.54-PM-768x123.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.32.54-PM.png 1250w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<p>We remodel our visual odometry module such that it can be used in unseen environments as well. To this end, we take 2 RGB frames from different timesteps and find the relative pose of the glasses at the 2 timesteps. <\/p>\n\n\n\n<p>We utilize the general Structure from Motion pipeline for this purpose starting with finding robust matched correspondences. We use Superpoint (and SuperGlue) for finding reliable correspondences between the 2 image frames. Using these matched correspondences, we find the Essential matrix. The E matrix is now decomposed to find the relative rotation and translation between the 2 frames.<\/p>\n\n\n\n<p>We observe that the error in rotation and translation increases with increasing the timestep gap between 2 frames. <\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Inertial odometry<\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"164\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.49.11-PM-1024x164.png\" alt=\"\" class=\"wp-image-261\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.49.11-PM-1024x164.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.49.11-PM-300x48.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.49.11-PM-768x123.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.49.11-PM.png 1250w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<p>We use the IDOL model with extension to 6-D pose instead of 5-D pose to be used with the visual odometry predictions.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Classifier<\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"164\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.49.52-PM-1024x164.png\" alt=\"\" class=\"wp-image-262\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.49.52-PM-1024x164.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.49.52-PM-300x48.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.49.52-PM-768x123.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.49.52-PM.png 1250w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<p>We aim to use the visual odometry module only when there is a major drift in the inertial odometry module. We use a classifier for this purpose. The classifier is trained on ground truth poses and the IDOL&#8217;s pose predictions using a hyperparameter threshold. The model architecture is a simple 3-layered MLP. <\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Visual-Inertial odometry<\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"165\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.50.35-PM-1024x165.png\" alt=\"\" class=\"wp-image-263\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.50.35-PM-1024x165.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.50.35-PM-300x48.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.50.35-PM-768x124.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.50.35-PM.png 1238w\" sizes=\"auto, (max-width: 706px) 89vw, (max-width: 767px) 82vw, 740px\" \/><\/figure>\n\n\n\n<p>When we combine the VO and IO predictions in a weighted fashion and turn on the camera based on the classifier&#8217;s predictions, we are able to get accurate pose predictions with very low power consumption.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Visual odometry We remodel our visual odometry module such that it can be used in unseen environments as well. To this end, we take 2 RGB frames from different timesteps and find the relative pose of the glasses at the 2 timesteps. We utilize the general Structure from Motion pipeline for this purpose starting with &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/2023\/12\/17\/fall-23-results\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Fall &#8217;23 progress&#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-255","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>Fall &#039;23 progress - 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\/12\/17\/fall-23-results\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Fall &#039;23 progress - Sparse Image and dual IMU localization for AR glasses\" \/>\n<meta property=\"og:description\" content=\"Visual odometry We remodel our visual odometry module such that it can be used in unseen environments as well. To this end, we take 2 RGB frames from different timesteps and find the relative pose of the glasses at the 2 timesteps. We utilize the general Structure from Motion pipeline for this purpose starting with &hellip; Continue reading &quot;Fall &#8217;23 progress&quot;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/2023\/12\/17\/fall-23-results\/\" \/>\n<meta property=\"og:site_name\" content=\"Sparse Image and dual IMU localization for AR glasses\" \/>\n<meta property=\"article:published_time\" content=\"2023-12-17T11:09:48+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-12-17T11:42:46+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/12\/Screen-Shot-2023-12-17-at-4.32.54-PM-1024x164.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=\"2 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\\\/12\\\/17\\\/fall-23-results\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team14\\\/2023\\\/12\\\/17\\\/fall-23-results\\\/\"},\"author\":{\"name\":\"dishanil\",\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team14\\\/#\\\/schema\\\/person\\\/bb79ff57dfca5bf74ee5d99394fc2d2a\"},\"headline\":\"Fall &#8217;23 progress\",\"datePublished\":\"2023-12-17T11:09:48+00:00\",\"dateModified\":\"2023-12-17T11:42:46+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team14\\\/2023\\\/12\\\/17\\\/fall-23-results\\\/\"},\"wordCount\":230,\"image\":{\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team14\\\/2023\\\/12\\\/17\\\/fall-23-results\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team14\\\/wp-content\\\/uploads\\\/sites\\\/91\\\/2023\\\/12\\\/Screen-Shot-2023-12-17-at-4.32.54-PM-1024x164.png\",\"articleSection\":[\"Introduction\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team14\\\/2023\\\/12\\\/17\\\/fall-23-results\\\/\",\"url\":\"https:\\\/\\\/mscvprojects.ri.cmu.edu\\\/f23team14\\\/2023\\\/12\\\/17\\\/fall-23-results\\\/\",\"name\":\"Fall '23 progress - 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