Learning-based Localizability Estimation for Robust LiDAR Localization

descriptionPublicationkeyboard_double_arrow_right Article , Conference object , Preprint 23 Oct 2022Embargo end date: 01 Jan 2022Publisher:IEEEJournal:2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Funded by:SNSF | Perceptive Dynamic Locomo..., EC | NI, EC | LeMo

Authors: Nubert, Julian; Walther, Etienne; Khattak, Shehryar; Hutter, Marco;

doi: 10.1109/iros47612.2022.9982257 , 10.3929/ethz-b-000558164 , 10.48550/arxiv.2203.05698

arXiv: 2203.05698

Learning-based Localizability Estimation for Robust LiDAR Localization

- Summary
- Subjects
- Related research
  (1)
- Metrics

Abstract

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

ISBN:978-1-6654-7927-1

ISBN:978-1-6654-7928-8

ISSN:2153-0858

Related Organizations

ETH Zurich
Switzerland

Keywords

FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Deep Learning in Robotics and Automation, Machine learning (artificial intelligence), Machine Learning (cs.LG), Mobile robot localization, Computer Science - Robotics, Artificial Intelligence (cs.AI), Localization and Mapping, Field robotics, Robotics (cs.RO)

1 Research products, page 1 of 1

L2M software on GitHub
IsRelatedTo

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	16
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%