Point Cloud Registration via Probabilistic Self-Update Local Correspondence and Line Vector Sets

TL;DR

This paper introduces a fast, accurate point cloud registration algorithm using probabilistic self-updating local correspondences and line vector sets, with a dual RANSAC model and early termination for efficiency.

Contribution

The paper presents a novel PCR method combining probabilistic self-updating local sets and a dual RANSAC approach, improving accuracy and efficiency over existing methods.

Findings

Achieves at least 10% RMSE improvement over state-of-the-art methods.

Demonstrates superior time efficiency on public datasets.

Utilizes a global early termination condition for faster convergence.

Abstract

Point cloud registration (PCR) is a fundamental task for integrating 3D observations in remote sensing applications. This paper proposes a fast and effective PCR algorithm utilizing probabilistic self-updating local correspondence and line vector sets. Our dual RANSAC interaction model comprises a global RANSAC evaluating the global correspondence set and a local RANSAC operating on dynamically updated local sets. Initially, these local sets are constructed using angle histogram statistics and line vector length preservation techniques. To improve accuracy, a probabilistic self-updating strategy refines the local sets after each interaction round. To reduce runtime, we introduce a global early termination condition that optimally balances accuracy and efficiency. Finally, a weighted singular value decomposition estimates the registration solution. Evaluations on public datasets demonstrate our algorithm achieves superior time efficiency and at least a 10% root mean square error improvement over state-of-the-art methods. The C++ source code is publicly available at https://github.com/ivpml84079/Probabilistic-Self-Update-Line-Vector-Set-Based-Point-Cloud-Registration.