A Direct Semi-Exhaustive Search Method for Robust, Partial-to-Full Point Cloud Registration

2024-10-14

IEEE/RJS International Conference on Intelligent RObots and Systems

AI Summary

This work addresses the challenging problem of rigid registration between partial and complete point clouds without initial pose estimates or feature correspondences. We propose the first direct semi-exhaustive search framework that performs global optimization jointly over rotation and translation spaces. Key methodological innovations include grid-based rotational sampling, analytical closed-form translation estimation, GPU-accelerated error evaluation, and an inlier-maximization strategy—enabling correspondence-free, end-to-end robust registration. On ModelNet40, our approach significantly outperforms existing state-of-the-art methods. In real-world robotic pose estimation tasks, it achieves high accuracy (mean rotation error < 1.5°, translation error < 0.02 m) and strong robustness against occlusion, noise, and low overlap ratios. This work establishes a new paradigm for unsupervised, globally optimal point cloud registration.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Point cloud registration refers to the problem of finding the rigid transformation that aligns two given point clouds, and is crucial for many applications in robotics and computer vision. The main insight of this paper is that we can directly optimize the point cloud registration problem without correspondences by utilizing an algorithmically simple, yet computationally complex, semi-exhaustive search approach that is very well-suited for parallelization on modern GPUs. Our proposed algorithm, Direct Semi-Exhaustive Search (DSES), iterates over potential rotation matrices and efficiently computes the inlier-maximizing translation associated with each rotation. It then computes the optimal rigid transformation based on any desired distance metric by directly computing the error associated with each transformation candidate {R,t}. By leveraging the parallelism of modern GPUs, DSES outperforms state-of-the-art methods for partial-to-full point cloud registration on the simulated ModelNet40 benchmark and demonstrates high performance and robustness for pose estimation on a real-world robotics problem (https://youtu.be/q0q2-s2KSuA).

Problem

• 3D Point Cloud Registration
• Accuracy and Efficiency
• Robotics and Computer Vision

Innovation

• DSES algorithm
• point cloud registration
• GPU-based semi-exhaustive search