Registration Tutorials

The PCL Registration API

In this document, we describe the point cloud registration API and its modules: the estimation and rejection of point correspondences, and the estimation of rigid transformations.

How to use iterative closest point

This tutorial gives an example of how to use the iterative closest point algorithm to see if one PointCloud is just a rigid transformation of another PointCloud.

  • Original

  • TestCode : examples/official/Registration/iterative_closest_point.py

How to incrementally register pairs of clouds

This document demonstrates using the Iterative Closest Point algorithm in order to incrementally register a series of point clouds two by two.

Interactive Iterative Closest Point

This tutorial will teach you how to build an interactive ICP program

How to use Normal Distributions Transform

This document demonstrates using the Normal Distributions Transform algorithm to register two large point clouds.

  • Original

  • TestCode : examples/official/Registration/normal_distributions_transform.py

In-hand scanner for small objects

This document shows how to use the In-hand scanner applications to obtain colored models of small objects with RGB-D cameras.

Robust pose estimation of rigid objects

In this tutorial, we show how to find the alignment pose of a rigid object in a scene with clutter and occlusions.

  • Original

  • TestCode : examples/official/Registration/alignment_prerejective.py