An approach to robust ICP initialization

12/10/2022
by   Alexander Kolpakov, et al.
0

In this note, we propose an approach to initialize the Iterative Closest Point (ICP) algorithm to match unlabelled point clouds related by rigid transformations. The method is based on matching the ellipsoids defined by the points' covariance matrices and then testing the various principal half-axes matchings that differ by elements of a finite reflection group. We derive bounds on the robustness of our approach to noise and numerical experiments confirm our theoretical findings.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/23/2019

Iterative Matching Point

In this paper, we propose a neural network-based point cloud registratio...
research
12/01/2020

Cross-modal registration using point clouds and graph-matching in the context of correlative microscopies

Correlative microscopy aims at combining two or more modalities to gain ...
research
11/08/2021

Isometry invariant shape recognition of projectively perturbed point clouds by the mergegram extending 0D persistence

Rigid shapes should be naturally compared up to rigid motion or isometry...
research
05/30/2022

Eigenvalue Bounds for Saddle-Point Systems with Singular Leading Blocks

We derive bounds on the eigenvalues of saddle-point matrices with singul...
research
03/05/2023

Robust affine feature matching via quadratic assignment on Grassmannians

GraNNI (Grassmannians for Nearest Neighbours Identification) a new algor...
research
09/12/2019

A New Approach to 3D ICP Covariance Estimation for Mobile Robotics

In mobile robotics, scan matching of point clouds using Iterative Closes...

Please sign up or login with your details

Forgot password? Click here to reset