Point cloud registration based on improved iterative closest point method

2012 ◽  
Vol 20 (9) ◽  
pp. 2068-2076 ◽  
Author(s):  
王欣 WANG Xin ◽  
张明明 ZHANG Ming-ming ◽  
于晓 YU Xiao ◽  
章明朝 ZHANG Ming-chao
Keyword(s):  
Point Cloud ◽  
Point Method ◽  
Iterative Closest Point ◽  
Point Cloud Registration ◽  
Closest Point Method

Iterative Closest Point Method with a Least Trimmed Squares Estimator

2012 ◽  
Vol 220-223 ◽  
pp. 1381-1384
Author(s):  
Ying Yan ◽  
Qi Xia ◽  
Ke Wang
Keyword(s):  
Statistical Method ◽  
Point Method ◽  
Experimental Results ◽  
New Method ◽  
Iterative Closest Point ◽  
Least Trimmed Squares ◽  
Residual Distribution ◽  
Robust Statistical Method ◽  
Closest Point Method

The iterative closest point (ICP) method is one of the most important methods for 2D/3D point registration. Robust statistical method is applied widely for improving the robustness of ICP. A new method that incorporates the Least Trimmed Squares (LTS) Estimator into the ICP is proposed in this paper. In this method, outliers are removed according to characteristics of residual distribution. A large number of experimental results show that the proposed method is robust and efficient.

Merging the point clouds of the head and ear by using the iterative closest point method

2016 ◽  
Vol 1 (3) ◽  
pp. 305
Author(s):  
Ming Zhang ◽  
Roger Ball ◽  
Nathaniel J. Martin ◽  
Yan Luximon
Keyword(s):  
Point Clouds ◽  
Point Method ◽  
Iterative Closest Point ◽  
Closest Point Method

Novel method for rail wear inspection based on the sparse iterative closest point method

2017 ◽  
Vol 28 (12) ◽  
pp. 125201 ◽  
Author(s):  
Bing Yi ◽  
Yue Yang ◽  
Qian Yi ◽  
Wanlin Dai ◽  
Xiongbing Li
Keyword(s):  
Point Method ◽  
Iterative Closest Point ◽  
Rail Wear ◽  
Closest Point Method ◽  
Novel Method
Sign in / Sign up

Export Citation Format

Share Document