3D Registration 三维点云配准 —— 基本概念和ICP算法的C++实现

未完

 

待读参考：

https://blog.csdn.net/kaspar1992/article/details/54836222

https://www.cnblogs.com/yin52133/archive/2012/07/21/2602562.html

https://blog.csdn.net/u011600592/article/details/70258097

https://blog.csdn.net/Ha_ku/article/details/79755623

https://www.cnblogs.com/21207-iHome/p/6038853.html

https://www.cnblogs.com/sddai/p/6129437.html

论文：方法比较 [Rusinkiewicz et Levoy, 2001], [GRUEN et AKCA, 2005]  et [AKCA, 2007]

 

课堂笔记：

RANSAC算法（RANdom SAmple Consensus随机抽样一致）

 

ICP算法（Iterative Closest Point迭代最近点）

目的: estimate transform between two dense sets of points

步骤：

1. Assign each point in {Set 1} to its nearest neighbor in {Set 2}
2. Estimate transformation parameters – e.g., least squares or robust least squares
3. Transform the points in {Set 1} using estimated parameters
4. Repeat steps 1-3 until change is very small

可行的预处理：去除离散的噪点。

 

 

扩展：PCL云点集