Introduction to Point Cloud Registration

Image Registration is the process of transforming different data sets into one coordinate system. For this research this go any further, a specialized type of registration called Point Cloud Registration is required. Point Cloud Registration is just like Image Registration except for the fact that images are in two dimensions while point clouds are in a three-dimensional space (due to depth). There are a couple of methods that are used to perform registration on point clouds such as Iterative Closest Point (ICP) and Random Sample Consensus (RANSAC), just to name a few. The Point Cloud Library has built-in classes dedicated to Point Cloud Registration that will the foundation to the registration done in this research.

The method that will be used in my research will be the Iterative Closest Point Algorithm, or ICP. The Iterative Closest Point Algorithm is a method used to minimize the distance between two clouds of points. For ICP to be perform efficiently, there needs to be considerable (about 45%) overlap between both of the point clouds. The process of Iterative Closest Point can be described as follows:

Step 1. Search for correspondences.
Step 2. Reject bad correspondences.
Step 3. Estimate a transformation using the good correspondences.
Step 4. Iterate.

If you would like to learn more about how ICP works in the Point Cloud Library, you can take a look at the PCL documentation: http://docs.pointclouds.org/trunk/a02953.html