Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Minimal Solvers for Point Cloud Matching with Statistical Deformations

Flood, Gabrielle LU ; Tegler, Erik LU ; Gillsjö, David LU orcid ; Heyden, Anders LU orcid and Åström, Kalle LU orcid (2022) 26th International Conference on Pattern Recognition, ICPR 2022
Abstract
An important issue in simultaneous localisation and mapping is how to match and merge individual local maps into one global map. This is addressed within the field of robotics and is crucial for multi-robot SLAM. There are a number of different ways to solve this task depending on the representation of the map. To take advantage of matching and merging methods that allow for deformations of the local maps it is important to find feature matches that capture such deformations. In this paper we present minimal solvers for point cloud matching using statistical deformations. The solvers use either three or four point matches. These solve for either rigid or similarity transformation as well as shape deformation in the direction of the most... (More)
An important issue in simultaneous localisation and mapping is how to match and merge individual local maps into one global map. This is addressed within the field of robotics and is crucial for multi-robot SLAM. There are a number of different ways to solve this task depending on the representation of the map. To take advantage of matching and merging methods that allow for deformations of the local maps it is important to find feature matches that capture such deformations. In this paper we present minimal solvers for point cloud matching using statistical deformations. The solvers use either three or four point matches. These solve for either rigid or similarity transformation as well as shape deformation in the direction of the most important modes of variation. Given an initial set of tentative matches based on, for example, feature descriptors or machine learning we use these solvers in a RANSAC loop to remove outliers among the tentative matches. We evaluate the methods on both synthetic and real data and compare them to RANSAC methods based on Procrustes and demonstrate that the proposed methods improve on the current state-of-the-art. (Less)
Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2022 26th International Conference on Pattern Recognition (ICPR)
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
26th International Conference on Pattern Recognition, ICPR 2022
conference location
Montreal, Canada
conference dates
2022-08-21 - 2022-08-25
external identifiers
  • scopus:85143618790
ISBN
978-1-6654-9063-4
DOI
10.1109/ICPR56361.2022.9956038
language
English
LU publication?
yes
id
49dd95bd-d71b-404d-a1f3-fdc1570cff00
date added to LUP
2022-12-05 16:49:20
date last changed
2023-12-03 22:56:27
@inproceedings{49dd95bd-d71b-404d-a1f3-fdc1570cff00,
  abstract     = {{An important issue in simultaneous localisation and mapping is how to match and merge individual local maps into one global map. This is addressed within the field of robotics and is crucial for multi-robot SLAM. There are a number of different ways to solve this task depending on the representation of the map. To take advantage of matching and merging methods that allow for deformations of the local maps it is important to find feature matches that capture such deformations. In this paper we present minimal solvers for point cloud matching using statistical deformations. The solvers use either three or four point matches. These solve for either rigid or similarity transformation as well as shape deformation in the direction of the most important modes of variation. Given an initial set of tentative matches based on, for example, feature descriptors or machine learning we use these solvers in a RANSAC loop to remove outliers among the tentative matches. We evaluate the methods on both synthetic and real data and compare them to RANSAC methods based on Procrustes and demonstrate that the proposed methods improve on the current state-of-the-art.}},
  author       = {{Flood, Gabrielle and Tegler, Erik and Gillsjö, David and Heyden, Anders and Åström, Kalle}},
  booktitle    = {{2022 26th International Conference on Pattern Recognition (ICPR)}},
  isbn         = {{978-1-6654-9063-4}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Minimal Solvers for Point Cloud Matching with Statistical Deformations}},
  url          = {{http://dx.doi.org/10.1109/ICPR56361.2022.9956038}},
  doi          = {{10.1109/ICPR56361.2022.9956038}},
  year         = {{2022}},
}