LEADER 00000nam  2200409   4500 
001    AAI1492632 
005    20121015121303.5 
008    121015s2011    ||||||||||||||||| ||eng d 
020    9781124650043 
035    (UMI)AAI1492632 
040    UMI|cUMI 
100 1  Aswadha Narayanan, Shyam Sundar 
245 10 Pose estimation for robotic disassembly using RANSAC with 
       line features 
300    71 p 
500    Source: Masters Abstracts International, Volume: 49-05, 
       page: 3348 
500    Adviser: Richard E. Groff 
502    Thesis (M.S.)--Clemson University, 2011 
520    In this thesis, a new technique to recognize and estimate 
       the pose of a given 3-D object from a single real image 
       provided known prior knowledge of its approximate 
       structure is proposed. Metrics to evaluate the correctness
       of a calculated pose are presented and analyzed. The 
       traditional and the more recent approaches used in solving
       this problem are explored and the various methodologies 
       adopted are discussed 
520    The first step in disassembling a given assembly from its 
       image is to recognize the attitude and translation of each
       of its constituent components---a fundamental problem 
       which is being addressed in this work. The proposed 
       algorithm does not depend on uniquely identifiable 3D 
       model surface features for its operation---this makes it 
       ideally suited for object recognition for assemblies. The 
       algorithm works well even for low-resolution occluded 
       object images taken under variable illumination conditions
       and heavy shadows and performs markedly better when these 
       factors are removed 
520    The algorithm uses a combination of various computer 
       vision concepts such as segmentation, corner detection and
       camera calibration, and subsequently adopts a line-based 
       object pose estimation technique (originally based on the 
       RANSAC algorithm) to settle on the best pose estimate. The
       novelty of the proposed technique lies in the specific way
       in which the poses are evaluated in the RANSAC-like 
       algorithm. In particular, line-based pose evaluation is 
       adopted where the line chamfer image is used to evaluate 
       the error distance between the projected model line and 
       the image edges. The correctness of the computed pose is 
       determined based on the number of line matches computed 
       using this error distance. As opposed to the RANSAC 
       algorithm where the search process is pseudo-random, we do
       an exhaustive pose search instead. Techniques to reduce 
       the search space by a large amount are discussed and 
520    The algorithm was used to estimate the pose of 28 objects 
       in 22 images, where some images contain multiple objects. 
       The algorithm has been found to work with a 3-D mismatch 
       error of less than 2.5cm in 90% of the cases and less than
       1cm error in 53% of the cases in the dataset used 
590    School code: 0050 
650  4 Engineering, Electronics and Electrical 
650  4 Engineering, Robotics 
650  4 Artificial Intelligence 
690    0544 
690    0771 
690    0800 
710 2  Clemson University.|bElectrical & Computer Engineering 
773 0  |tMasters Abstracts International|g49-05 
856 40 |uhttp://pqdd.sinica.edu.tw/twdaoapp/servlet/