{"id":201,"date":"2016-10-21T14:00:30","date_gmt":"2016-10-21T18:00:30","guid":{"rendered":"http:\/\/mrsdprojects.ri.cmu.edu\/2016teamg\/?page_id=201"},"modified":"2017-05-11T16:47:10","modified_gmt":"2017-05-11T20:47:10","slug":"system-requirements","status":"publish","type":"page","link":"https:\/\/mrsdprojects.ri.cmu.edu\/2016teamg\/system-requirements\/","title":{"rendered":"System requirements"},"content":{"rendered":"

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Objectives<\/span><\/td>\nFunctional requirement<\/span><\/td>\nPerformance requirement<\/span><\/td>\nNon-functional requirements<\/span><\/td>\n<\/tr>\n
Sensor Noise<\/span><\/td>\nCreate an efficient data collection plan by optimizing the amount of data collected and the computational power required to process it.<\/span><\/td>\n<\/td>\n<\/td>\n<\/tr>\n
Obtain the noise characteristic graphs for read noise, shot noise, PRNU and thermal noise.<\/span><\/td>\n<\/td>\n<\/td>\n<\/tr>\n
<\/td>\nUse the dark and flat frames corresponding to each camera and exposure value to correct the image from PRNU and thermal noise, and obtain a measure of the read and shot noise for the given camera and exposure value from the noise characteristic plot. \u00a0<\/span><\/td>\n<\/td>\n<\/td>\n<\/tr>\n
Color calibration<\/span><\/td>\n\n
    \n
  1. Develop automatic color checker detection algorithm<\/span><\/li>\n
  2. Generate color mapping function – Map sensor-observed colors to the standard color space<\/span><\/li>\n<\/ol>\n<\/td>\n
\n
    \n
  1. color calibration: 60% color patch detection accuracy – Be able to find all color patch values of at least 60% of input images.<\/span><\/li>\n<\/ol>\n<\/td>\n
<\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
Geometric calibration <\/span><\/td>\n2.Testing Camera calibration algorithm efficiency and accuracy <\/span><\/td>\n2-1. Estimation time for 140 cameras should less than 8 hours<\/span><\/p>\n

2-2 reprojection error should less than 0.1 pixels(real images)<\/span><\/td>\n

2-3 3D reconstruction result improvement. <\/span><\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
Motion planning #1 Virtual Camera Images<\/span><\/td>\n1. Validation of Geometric Calibration Algorithm<\/span><\/td>\n1. Reprojection error should less than 0.1 pixels<\/span><\/p>\n

2. Export Camera Data and Object Data with float64-bit precision<\/span><\/td>\n

\n
    \n
  1. PBR(Physically Based Rendering)- Photorealistic Images<\/span><\/li>\n<\/ol>\n<\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
Motion planning#2 robot arm simulation and manipulation<\/span><\/td>\n1.Perform ABB robot arm simulation using RobotStudio<\/span><\/p>\n

2.load the path file and generate the machine code.<\/span><\/p>\n

3.Navigate the ABB robot arm in designed trajectory <\/span><\/p>\n

4.Robot arm would not have collision with the dome<\/span><\/td>\n

1.5 min prepare time for navigating the robot arm in designed trajectory<\/span><\/td>\n<\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
 <\/p>\n

camera FOV simulation<\/span><\/td>\n

1.Successfully generate an optimized path for ABB Robot Arm motion satisfying the motion constraints.<\/span><\/p>\n

2.<\/span>Output 3D points should be able to connect with the RobotStudio.<\/span><\/td>\n

1. Select 200 optimized 3D points with the medium projection size, more than 85% coverage and as less variance in projection distribution as possible.<\/span><\/p>\n

2. An image capturing complete within 2 hours.<\/span><\/p>\n

3. Path calculating algorithms complete within 8 hours.<\/span><\/td>\n

<\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
System integration <\/span><\/td>\n1.ABB robot arm should move the calibration target in a designed trajectory.<\/span><\/p>\n

2.ABB robot arm would trigger the camera in designed position<\/span><\/td>\n

1.two click operation->10 minutes prepare time<\/span><\/p>\n

2.image result should match with the simulated images error < 5%( ex. cover percentage or position of calibration target or number of images.. TBD)<\/span><\/td>\n

<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

  Objectives Functional requirement Performance requirement Non-functional requirements Sensor Noise Create an efficient data collection plan by optimizing the amount of data collected and the computational power required to process it. Obtain the noise characteristic graphs for read noise, shot […]<\/p>\n","protected":false},"author":78,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-201","page","type-page","status-publish","hentry","clearfix"],"_links":{"self":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2016teamg\/wp-json\/wp\/v2\/pages\/201","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2016teamg\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2016teamg\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2016teamg\/wp-json\/wp\/v2\/users\/78"}],"replies":[{"embeddable":true,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2016teamg\/wp-json\/wp\/v2\/comments?post=201"}],"version-history":[{"count":8,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2016teamg\/wp-json\/wp\/v2\/pages\/201\/revisions"}],"predecessor-version":[{"id":834,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2016teamg\/wp-json\/wp\/v2\/pages\/201\/revisions\/834"}],"wp:attachment":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2016teamg\/wp-json\/wp\/v2\/media?parent=201"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}