Arun, K., Huang, T., Blostein, S.: Least-squares fitting of two 3D point sets. IEEE Transactions on Pattern Analysis and Machine Intelligence 9 (1987) 698--700 Home/Search Document Not in Database Summary Related Articles Check |
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....R #(t) 11) which can be solved for R by the standard MoorePenrose inverse i = d# [ d# ] 1 . 12) As in the previous cases, the Moore Penrose inverse solution computes a zero noise case. Alternatively, the singular value decomposition (SVD) approach originally reported in [2] provides a least square solution subject to the additional constraint that SO(3) The reader is referred to [2] 17] for a detailed exposition of the SVD solution method. III. Performance Evaluation We evaluated the practical utility of the proposed calibration method by synthesizing ....
....As in the previous cases, the Moore Penrose inverse solution computes a zero noise case. Alternatively, the singular value decomposition (SVD) approach originally reported in [2] provides a least square solution subject to the additional constraint that SO(3) The reader is referred to [2], 17] for a detailed exposition of the SVD solution method. III. Performance Evaluation We evaluated the practical utility of the proposed calibration method by synthesizing simulated timesampled sensor data with a #true# sensor alignment i R, computing an estimate of this rotation matrix R ....
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K. S. Arun, T. S. Huang, and S. D. Blostein, #Leastsquares fitting of two 3-D point sets,# IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-9, no. 5, pp. 698#700, 1987.
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....Thus P 2 ) and 2 (P 3 R P 1 ) Figure 5.4d) Finding the optimal rotation and translation which minimizes the sum of squared distances between corresponding points is a well known classic problem of pose estimation. Several methods have been suggested to solve this problem analytically [5, 47]. We follow the method of Arun et al. 5] Given 2 sets of points fP i g i=0 and f i=0 and given a correspondence between them (without loss of generality, we assume point P i corresponds to point P i ) 1. Calculate the centeroids P and P of the two sets. 2. Translate each set so that ....
....5.4d) Finding the optimal rotation and translation which minimizes the sum of squared distances between corresponding points is a well known classic problem of pose estimation. Several methods have been suggested to solve this problem analytically [5, 47] We follow the method of Arun et al. [5]; Given 2 sets of points fP i g i=0 and f i=0 and given a correspondence between them (without loss of generality, we assume point P i corresponds to point P i ) 1. Calculate the centeroids P and P of the two sets. 2. Translate each set so that its centroid aligns with the origin. i.e. Q ....
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K.S. Arun, T.S. Huang, and S.D. Blostein. Least squares fitting of two 3D point sets. IEEE Trans. on Pattern Analysis and Machine Intelligence, 9(5):698--700, Sept. 1987.
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....Computing System Figure 2. Head mounted display tracking HMD system can run for approximately 3 4 hours before it needs to be recharged. # # # # # # # # # # # # # # # # # # # # # The HMD mounted Bats are used to obtain a least squares estimate of the position and orientation of the user s head [3] (see Figure 2 for an overview of the system) The HMD software object is responsible for taking in the raw Bat readings, and giving the best estimate of the current head position and orientation. The software object takes the three position readings from the Bats and calculates the orientation as ....
K. S. Arun, T. Huang, and S. D. Blostein. Least-Squares Fitting of Two 3-D Point Sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(5):698--700, September 1987.
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....not converge at all since many spurious matches will be included. At the end of this step, two corresponding point sets, PM : p i and PD : q i are said to be established. We recover the incremental 3D transformation (rotation and translation) of step 2. in the least squares sense as follows [2]: Calculate H= ND i=1 (p i p c ) q i q c ) p c ,q c )are the centroids of the point sets (PM ,PD ) Find the SVD of H such that H = U#V . The rotation matrix relating the two point sets is then given by R = VU . The translation between the two point sets is ....
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Arun, K., Huang, T., Blostein, S.: Least-squares fitting of two 3D point sets. IEEE Transactions on Pattern Analysis and Machine Intelligence 9 (1987) 698--700
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K. S. Arun, T. S. Huang, and S. D. Blostein. Least-squares fitting of two 3-d point sets. IEEE Trans. Pattern Anal. Mach. Intell., 9(5):698-- 700, 1987.
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Huang T.S. Blostein S.D. Arun, K.S. Least-squares fitting of two 3d point sets. Transactions on Pattern Analysis and Machine Intelligence, 9(5):698--700, 1987.
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Arun, K., Huang, T., Blostein, S.: Least-squares fitting of two 3D point sets. IEEE Transactions on Pattern Analysis and Machine Intelligence 9 (1987) 698--700
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K. S. Arun, T. S. Huang, and S. D. Blostein. Least square fitting of two 3-d point sets. Transactions on Pattern Analysis and Machine Intelligence, 9(5), 1987.
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K.S. Arun, T.S. Huang, and S.D. Blostein. Least-squares fitting of two 3D point sets. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), 9:698--700, 1987. 8
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K. S. Arun, T. S. Huang, and S. D. Blostein. Least-squares fitting of two 3-D point sets. IEEE Trans. on Pattern Analysis and Machine Intelligence, 9(5):698--700, Sept. 1987.
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K.S. Arun, T.S. Huang, and S.D. Blostein. Least-squares fitting of two 3-D point sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-9(5):698-- 700, September 1987.
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K.S. Arun, T.S. Huang, and S.D. Blostein. Least-squares fitting of two 3-D point sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(5):698--700, 1987.
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K.S. Arun, T.S. Huang and S.D. Blostein. Least-Squares Fitting of Two 3-D Point Sets. IEEE Trans. Pattern Anal. Machine Intell., 9(5):698--700, 1987.
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K.S. Arun, T.S. Huang and S.D. Blostein. Least-Squares Fitting of Two 3-D Point Sets. IEEE Trans. Pattern Anal. Machine Intell., 9(5):698--700, 1987.
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Arun KS, Huang TS, Blostein SD. Least-square fitting of two 3d point sets. IEEE Trans Pattern Anal Machine Intell. 1987; 9: 698-700.
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