Robust Rigid Point Registration based on Convolution of Adaptive Gaussian Mixture Models

TL;DR

This paper introduces a robust and accurate method for 3D rigid point cloud registration using adaptive Gaussian Mixture Models and global probability alignment, effective in noisy and complex environments.

Contribution

It proposes a novel adaptive GMM architecture and a dual global probability alignment technique for improved point cloud registration accuracy.

Findings

Demonstrates superior robustness in noisy, occluded, and outlier-rich environments.

Achieves high accuracy across diverse 2D and 3D datasets.

Efficient optimization with large convergence zones.

Abstract

Matching 3D rigid point clouds in complex environments robustly and accurately is still a core technique used in many applications. This paper proposes a new architecture combining error estimation from sample covariances and dual global probability alignment based on the convolution of adaptive Gaussian Mixture Models (GMM) from point clouds. Firstly, a novel adaptive GMM is defined using probability distributions from the corresponding points. Then rigid point cloud alignment is performed by maximizing the global probability from the convolution of dual adaptive GMMs in the whole 2D or 3D space, which can be efficiently optimized and has a large zone of accurate convergence. Thousands of trials have been conducted on 200 models from public 2D and 3D datasets to demonstrate superior robustness and accuracy in complex environments with unpredictable noise, outliers, occlusion, initial rotation, shape and missing points.