---
_id: '1966'
abstract:
- lang: eng
text: Point cloud registration is often used in fields like SLAM where the overlap
of two consecutive point clouds is large. But in fields like multi-sensor fusion
of point clouds and LiDAR-based localization, there is a high chance of registering
non-overlapping point cloud pairs. Since in such cases, the result will always
be a wrong transformation, it is useful to evaluate the alignability of the point
cloud pairs prior to the registration. In this paper, an algorithm is presented
that predicts the alignability of two point clouds based on the minimum distances
of descriptors. It calculates statistical measures describing the minimum distances
and classifies the point cloud pairs. The paper shows that it is possible to predict
the alignability and evaluates the runtime compared to registration algorithms,
as well as the ignoring of the largest minimum distances.
author:
- first_name: André
full_name: Kirsch, André
id: '229807'
last_name: Kirsch
- first_name: Andrei
full_name: Günter, Andrei
id: '225747'
last_name: Günter
orcid: 0000-0002-7836-9700
- first_name: Matthias
full_name: König, Matthias
id: '213498'
last_name: König
orcid: 0000-0002-4915-0750
citation:
alphadin: 'Kirsch, André ; Günter,
Andrei ; König, Matthias:
Predicting Alignability of Point Cloud Pairs for Point Cloud Registration Using
Features. In: 12th International Conference on Pattern Recognition Systems :
IEEE, 2022'
ama: 'Kirsch A, Günter A, König M. Predicting Alignability of Point Cloud Pairs
for Point Cloud Registration Using Features. In: 12th International Conference
on Pattern Recognition Systems. IEEE; 2022. doi:10.1109/ICPRS54038.2022.9854071'
apa: 'Kirsch, A., Günter, A., & König, M. (2022). Predicting Alignability of
Point Cloud Pairs for Point Cloud Registration Using Features. In 12th International
Conference on Pattern Recognition Systems. Saint-Étienne: IEEE. https://doi.org/10.1109/ICPRS54038.2022.9854071'
bibtex: '@inproceedings{Kirsch_Günter_König_2022, title={Predicting Alignability
of Point Cloud Pairs for Point Cloud Registration Using Features}, DOI={10.1109/ICPRS54038.2022.9854071},
booktitle={12th International Conference on Pattern Recognition Systems}, publisher={IEEE},
author={Kirsch, André and Günter, Andrei and König, Matthias}, year={2022} }'
chicago: Kirsch, André, Andrei Günter, and Matthias König. “Predicting Alignability
of Point Cloud Pairs for Point Cloud Registration Using Features.” In 12th
International Conference on Pattern Recognition Systems. IEEE, 2022. https://doi.org/10.1109/ICPRS54038.2022.9854071.
ieee: A. Kirsch, A. Günter, and M. König, “Predicting Alignability of Point Cloud
Pairs for Point Cloud Registration Using Features,” in 12th International Conference
on Pattern Recognition Systems, Saint-Étienne, 2022.
mla: Kirsch, André, et al. “Predicting Alignability of Point Cloud Pairs for Point
Cloud Registration Using Features.” 12th International Conference on Pattern
Recognition Systems, IEEE, 2022, doi:10.1109/ICPRS54038.2022.9854071.
short: 'A. Kirsch, A. Günter, M. König, in: 12th International Conference on Pattern
Recognition Systems, IEEE, 2022.'
conference:
end_date: 2022-06-10
location: Saint-Étienne
name: 12th International Conference on Pattern Recognition Systems
start_date: 2022-06-07
date_created: 2022-05-24T13:55:09Z
date_updated: 2023-06-16T11:32:43Z
doi: 10.1109/ICPRS54038.2022.9854071
keyword:
- alignability prediction
- point cloud registration
- overlap metric
- descriptors
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9854071
project:
- _id: A838A474-C7DA-11E9-B0AE-1F4CB252D58D
name: 'DORIOT: Dynamische Laufzeitumgebung für organisch (dis-)aggregierende IoT-Prozesse'
publication: 12th International Conference on Pattern Recognition Systems
publication_status: published
publisher: IEEE
quality_controlled: '1'
status: public
title: Predicting Alignability of Point Cloud Pairs for Point Cloud Registration Using
Features
type: conference
user_id: '216459'
year: '2022'
...