Robust euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm

被引:338
|
作者
Chetverikov, D
Stepanov, D
Krsek, P
机构
[1] Eotvos Lorand Univ, Comp & Automat Res Inst, H-1111 Budapest, Hungary
[2] Czech Tech Univ, FEE, Ctr Appl Cybernet, Prague 6, Czech Republic
关键词
registration; point sets; iterative closest point; least trimmed squares; robustness;
D O I
10.1016/j.imavis.2004.05.007
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The problem of geometric alignment of two roughly pre-registered, partially overlapping, rigid, noisy 3D point sets is considered. A new natural and simple, robustified extension of the popular Iterative Closest Point (ICP) algorithm [IEEE Trans. Pattern Anal. Machine Intell. 14 (1992) 239] is presented, called Trimmed ICP (TrICP). The new algorithm is based on the consistent use of the Least Trimmed Squares approach in all phases of the operation. Convergence is proved and an efficient implementation is discussed. TrICP is fast, applicable to overlaps under 50%, robust to erroneous and incomplete measurements, and has easy-to-set parameters. ICP is a special case of TrICP when the overlap parameter is 100%. Results of a performance evaluation study on the SQUID database of 1100 shapes are presented. The tests compare TrICP and the Iterative Closest Reciprocal Point algorithm [Fifth International Conference on Computer Vision, 1995]. (C) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码:299 / 309
页数:11
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