Work

An Approach to Robust ICP Initialization

Year: 2023

Type: article

Abstract: 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.

Source: IEEE Transactions on Pattern Analysis and Machine Intelligence

DOI: 10.1109/tpami.2023.3287468

Authors: Alexander Kolpakov, Michael Werman

Institutions: University of Neuchâtel, Hebrew University of Jerusalem

Language: English

Topic: 3D Shape Modeling and Analysis

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Israel Science Foundation

Award: Higher-dimensional geometric and combinatorial structures