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Least-Squares Fitting of Two 3-D Point Sets
May 1987 (vol. 9 no. 5)
pp. 698-700
K. S. Arun, Coordinated Science Laboratory, University of Illinois, Urbana, IL 61801.
T. S. Huang, Coordinated Science Laboratory, University of Illinois, Urbana, IL 61801.
S. D. Blostein, Coordinated Science Laboratory, University of Illinois, Urbana, IL 61801.
Two point sets {pi} and {p'i}; i = 1, 2,..., N are related by p'i = Rpi + T + Ni, where R is a rotation matrix, T a translation vector, and Ni a noise vector. Given {pi} and {p'i}, we present an algorithm for finding the least-squares solution of R and T, which is based on the singular value decomposition (SVD) of a 3 × 3 matrix. This new algorithm is compared to two earlier algorithms with respect to computer time requirements.
Citation:
K. S. Arun, T. S. Huang, S. D. Blostein, "Least-Squares Fitting of Two 3-D Point Sets," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 9, no. 5, pp. 698-700, May 1987, doi:10.1109/TPAMI.1987.4767965
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