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.
Original language | American English |
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Pages (from-to) | 12685-12691 |
Number of pages | 7 |
Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Volume | 45 |
Issue number | 10 |
DOIs | |
State | Published - 2023 |
Bibliographical note
Publisher Copyright:IEEE
Keywords
- Ellipsoids
- Iterative closest point algorithm
- Noise measurement
- Point cloud compression
- Random variables
- Symmetric matrices
- Three-dimensional displays