DUGMA: Dynamic Uncertainty-Based Gaussian Mixture Alignment

by   Can Pu, et al.

Registering accurately point clouds from a cheap low-resolution sensor is a challenging task. Existing rigid registration methods failed to use the physical 3D uncertainty distribution of each point from a real sensor in the dynamic alignment process mainly because the uncertainty model for a point is static and invariant and it is hard to describe the change of these physical uncertainty models in the registration process. Additionally, the existing Gaussian mixture alignment architecture cannot be efficiently implement these dynamic changes. This paper proposes a simple architecture combining error estimation from sample covariances and dual dynamic global probability alignment using the convolution of uncertainty-based Gaussian Mixture Models (GMM) from point clouds. Firstly, we propose an efficient way to describe the change of each 3D uncertainty model, which represents the structure of the point cloud much better. Unlike the invariant GMM (representing a fixed point cloud) in traditional Gaussian mixture alignment, we use two uncertainty-based GMMs that change and interact with each other in each iteration. In order to have a wider basin of convergence than other local algorithms, we design a more robust energy function by convolving efficiently the two GMMs over the whole 3D space. Tens of thousands of trials have been conducted on hundreds of models from multiple datasets to demonstrate the proposed method's superior performance compared with the current state-of-the-art methods. The new dataset and code is available from https://github.com/Canpu999


page 1

page 2

page 3

page 4


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

Matching 3D rigid point clouds in complex environments robustly and accu...

Overlap-guided Gaussian Mixture Models for Point Cloud Registration

Probabilistic 3D point cloud registration methods have shown competitive...

DeepGMR: Learning Latent Gaussian Mixture Models for Registration

Point cloud registration is a fundamental problem in 3D computer vision,...

PointNetLK: Robust & Efficient Point Cloud Registration using PointNet

PointNet has revolutionized how we think about representing point clouds...

A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments

Global registration using 3D point clouds is a crucial technology for mo...

Probabilistic Point Cloud Modeling via Self-Organizing Gaussian Mixture Models

This letter presents a continuous probabilistic modeling methodology for...

Incremental Multimodal Surface Mapping via Self-Organizing Gaussian Mixture Models

This letter describes an incremental multimodal surface mapping methodol...

Please sign up or login with your details

Forgot password? Click here to reset