Least-squares fitting of two 3-D point sets

Arun, K. S.<sup>1</sup>; Huang, T. S.<sup>1</sup>; Blostein, S. D.<sup>1</sup>

Affiliations

¹ Coordinated Science Laboratory, University of Illinois, Urbana, IL 61801

Abstract and keywords

Two point sets {p_i} and {p'_i}; i = 1, 2,..., N are related by p'_i = Rp_i + T + N_i, where R is a rotation matrix, T a translation vector, and Ni a noise vector. Given {p_i} 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.

Keywords: Computer vision; least-squares; motion estimation; quaternion; singular value decomposition

Links

DOI: 10.1109/TPAMI.1987.4767965

PubMed: 21869429

WoS: A1987J739300012

History

Received: 1986-07-02

Revised: 1987-04-09

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