{"id":875,"date":"2011-01-01T11:04:10","date_gmt":"2011-01-01T18:04:10","guid":{"rendered":"http:\/\/mcclanahoochie.com\/blog\/?post_type=portfolio&p=875"},"modified":"2023-06-10T10:32:26","modified_gmt":"2023-06-10T17:32:26","slug":"igvc-robot","status":"publish","type":"post","link":"https:\/\/mcclanahoochie.com\/blog\/2011\/01\/igvc-robot\/","title":{"rendered":"IGVC Robot"},"content":{"rendered":"

July 2009<\/h3>\n

\"\"<\/a><\/p>\n

I competed in the 2009\u00a0Intelligent Ground Vehicle\u00a0Competition<\/a>, on the Georgia Tech\u00a0RoboJackets<\/a> Team.\u00a0Our robot: Candi.<\/p>\n

I developed the computer vision algorithms to navigate an autonomous vehicle using only a vision camera.<\/p>\n

Our team ranked\u00a06th place<\/em> nationwide<\/strong>.\u00a0Competition photos\u00a0here.
\n<\/a>
\nI wrote all of the vision and mapping code!<\/em><\/span> The robot navigated using a single<\/em> camera only.<\/p>\n

A screenshot of the robot code running on my laptop:<\/p>\n

\"\"<\/a><\/p>\n

A picture of the robot in action:<\/p>\n

\"\"<\/a><\/p>\n

 <\/p>\n

Quick and dirty breakdown of the IGVC robot’s computer vision navigation:<\/p>\n

    \n
  1. Capture firewire camera frame<\/li>\n
  2. Apply an inverse perspective transform. This transform makes both near and far off objects a normalized size, forcing the assumption that the course is a flat plane. This makes it easier to process assuming the camera is\u00a0looking<\/span> straight down on a planer world, than dealing with 3D coordinates.<\/li>\n
  3. A region of interest box is drawn on the area of the input image immediately in front of the robot, and assumes that whatever color fills that region is the color that is traversable land, and everything else is an obstacle.<\/span><\/li>\n
  4. The average of RGB ratios is used to threshold the transformed image in to a binary\u00a0image of\u00a0‘traverse-able\u00a0(white) or obstacle (black) . This image is mapped into a world space using a homography matrix.<\/li>\n
  5. Simultaneously, the input image is also converted to\u00a0greyscale, and a feature tracker finds and tracks features for alternating frames of motion. The features found are filtered using RANSAC, and a homography matrix that maps between frames is computed. <\/span><\/li>\n
  6. The homography matrix is used to translate\/rotate the robot around in the world-map.\u00a0The world-map is built up as the robot moves, where black is obstacle, white is traversable, and gray is\u00a0unknown. The map slowly decays back to gray to prevent loop closure errors from building up.<\/span><\/li>\n
  7. Scan lines protrude from the robot’s center on the world map in a semi-circle, scanning along to find dark (obstacle) pixels. The scan line with the most white pixels (traversability) is chosen, and the robot turns and moves in the new direction.<\/span><\/li>\n<\/ol>\n

     <\/p>\n

    \"6th<\/a>
    6th Place Award!<\/figcaption><\/figure>\n

     <\/p>\n","protected":false},"excerpt":{"rendered":"

    July 2009 I competed in the 2009\u00a0Intelligent Ground Vehicle\u00a0Competition, on the Georgia Tech\u00a0RoboJackets Team.\u00a0Our robot: Candi. I developed the computer vision algorithms to navigate an autonomous vehicle using only a vision camera. Our team ranked\u00a06th place nationwide.\u00a0Competition photos\u00a0here. I wrote all of the vision and mapping code! The robot navigated using a single camera only. … Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[1],"tags":[139,113,138,54,92,101,29,137],"class_list":["post-875","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-competition","tag-computer-vision","tag-igvc","tag-image-processing","tag-opencv","tag-programming","tag-projects","tag-robot"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/pZdXI-e7","jetpack-related-posts":[{"id":1201,"url":"https:\/\/mcclanahoochie.com\/blog\/2011\/05\/ai-learning-portfolio\/","url_meta":{"origin":875,"position":0},"title":"AI Learning Portfolio","author":"mcclanahoochie","date":"May 4, 2011","format":false,"excerpt":"As a final assignment\/write-up for my CS6601 Artificial Intelligence class at Georgia Tech, this\u00a0learning portfolio was made to summarize what I had learned throughout the course... 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It only uses\u00a0Java on the CPU for now, but in my free time I'll be porting the code to OpenGL ES to\u2026","rel":"","context":"In \"android\"","block_context":{"text":"android","link":"https:\/\/mcclanahoochie.com\/blog\/tag\/android\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/mcclanahoochie.com\/blog\/wp-content\/uploads\/2011\/01\/device-sobel-2-small.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1153,"url":"https:\/\/mcclanahoochie.com\/blog\/2011\/04\/computer-vision-on-android-opencv\/","url_meta":{"origin":875,"position":2},"title":"Computer Vision on Android with OpenCV","author":"mcclanahoochie","date":"April 8, 2011","format":false,"excerpt":"March 2011 With the help of Motodev Studio for Android, I've\u00a0extracted\u00a0the\u00a0android-opencv JNI\u00a0camera example and spawned a fork of my original computer vision app,\u00a0Viewer, to an OpenCV version: ViewerCV. Both are\u00a0available on Git Hub\u00a0as open source software example of doing Computer Vision on Android with OpenCV. Viewer Features: *FAST Features (default\u2026","rel":"","context":"In \"android\"","block_context":{"text":"android","link":"https:\/\/mcclanahoochie.com\/blog\/tag\/android\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/mcclanahoochie.com\/blog\/wp-content\/uploads\/2011\/04\/viewercv1.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1731,"url":"https:\/\/mcclanahoochie.com\/blog\/2011\/08\/image-processing-with-libjacket-opencv\/","url_meta":{"origin":875,"position":3},"title":"Image processing with LibJacket + OpenCV","author":"mcclanahoochie","date":"August 24, 2011","format":false,"excerpt":"Update: one year later:\u00a0ArrayFire+OpenCV The OpenCV library is the de-facto standard for doing computer vision and image processing research projects. OpenCV includes several hundreds of computer vision algorithms, aimed for use in real-time vision applications. LibJacket is a matrix library built on CUDA. LibJacket offers hundreds of general matrix and\u2026","rel":"","context":"In \"arrayfire\"","block_context":{"text":"arrayfire","link":"https:\/\/mcclanahoochie.com\/blog\/tag\/arrayfire\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/mcclanahoochie.com\/blog\/wp-content\/uploads\/2011\/08\/Screen-shot-2011-08-24-at-2.42.52-PM-1024x640.png?resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/mcclanahoochie.com\/blog\/wp-content\/uploads\/2011\/08\/Screen-shot-2011-08-24-at-2.42.52-PM-1024x640.png?resize=350%2C200 1x, https:\/\/i0.wp.com\/mcclanahoochie.com\/blog\/wp-content\/uploads\/2011\/08\/Screen-shot-2011-08-24-at-2.42.52-PM-1024x640.png?resize=525%2C300 1.5x"},"classes":[]},{"id":1966,"url":"https:\/\/mcclanahoochie.com\/blog\/2011\/12\/computer-vision-learning-portfolio\/","url_meta":{"origin":875,"position":4},"title":"Computer Vision Learning Portfolio","author":"mcclanahoochie","date":"December 12, 2011","format":false,"excerpt":"This page constitutes my required\u00a0external\u00a0learning portfolio for CS 7495, Computer Vision, taken in Fall 2011. 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With\u00a0Python and OpenCV,\u00a0I got the system to find contours with the camera, and tell the laser to draw\u00a0them in\u2026","rel":"","context":"In \"computer vision\"","block_context":{"text":"computer vision","link":"https:\/\/mcclanahoochie.com\/blog\/tag\/computer-vision\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/mcclanahoochie.com\/blog\/wp-content\/uploads\/2011\/01\/laser_contours2.png?resize=350%2C200","width":350,"height":200},"classes":[]}],"jetpack_likes_enabled":false,"_links":{"self":[{"href":"https:\/\/mcclanahoochie.com\/blog\/wp-json\/wp\/v2\/posts\/875","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mcclanahoochie.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mcclanahoochie.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mcclanahoochie.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mcclanahoochie.com\/blog\/wp-json\/wp\/v2\/comments?post=875"}],"version-history":[{"count":0,"href":"https:\/\/mcclanahoochie.com\/blog\/wp-json\/wp\/v2\/posts\/875\/revisions"}],"wp:attachment":[{"href":"https:\/\/mcclanahoochie.com\/blog\/wp-json\/wp\/v2\/media?parent=875"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mcclanahoochie.com\/blog\/wp-json\/wp\/v2\/categories?post=875"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mcclanahoochie.com\/blog\/wp-json\/wp\/v2\/tags?post=875"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}