IPT: Iterative Pairing and Transformation for Multiple Point Cloud Registration.

Area of Science:

• Computer Vision
• Robotics
• Geomatics

Background:

• Accurate 3D mapping relies on registering multiple point clouds (MPC) into a cohesive global map.
• Challenges include identifying registrable pairs, correspondence detection, relative transformation estimation, and global alignment.

Purpose of the Study:

• To propose an Iterative Pairing and Transformation (IPT) framework for robust MPC registration.
• To enhance the accuracy and efficiency of 3D global map generation.

Main Methods:

• Introduced a Registrability Graph (RG) to model pairwise point cloud registerability.
• Employed adaptive coarse registration (RANSAC) and learning-based pairwise registration.
• Utilized iterative local pairing and global optimization until convergence.

Main Results:

• The Registrability Graph effectively prunes unregistrable pairs, improving efficiency.
• The iterative process ensures globally consistent poses and an accurate global map.
• IPT demonstrated superior performance compared to existing state-of-the-art MPC registration algorithms.

Conclusions:

• The IPT framework provides a novel and effective solution for challenging MPC registration problems.
• This method significantly advances the state-of-the-art in 3D mapping applications.
• The proposed approach offers a robust pathway for generating high-quality global maps from diverse point cloud data.