2021
DOI: 10.3390/s21124103
|View full text |Cite
|
Sign up to set email alerts
|

Condition-Invariant Robot Localization Using Global Sequence Alignment of Deep Features

Abstract: Localization is one of the essential process in robotics, as it plays an important role in autonomous navigation, simultaneous localization, and mapping for mobile robots. As robots perform large-scale and long-term operations, identifying the same locations in a changing environment has become an important problem. In this paper, we describe a robust visual localization system under severe appearance changes. First, a robust feature extraction method based on a deep variational autoencoder is described to cal… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2021
2021
2022
2022

Publication Types

Select...
2

Relationship

1
1

Authors

Journals

citations
Cited by 2 publications
(2 citation statements)
references
References 33 publications
0
2
0
Order By: Relevance
“…The use of processed sensor data rather than raw LiDAR or visual data in KFs is common, to improve the robustness of the extracted features. For example, Oh et al [ 28 ] use a feature extractor based on a deep variational autoencoder, then use sequences of these features over time to aid the KF. Dong et al [ 29 ] attack the other half of the equation, processing the GNSS signal to extract information about the GNSS carrier phase to improve the fused localization.…”
Section: Related Workmentioning
confidence: 99%
“…The use of processed sensor data rather than raw LiDAR or visual data in KFs is common, to improve the robustness of the extracted features. For example, Oh et al [ 28 ] use a feature extractor based on a deep variational autoencoder, then use sequences of these features over time to aid the KF. Dong et al [ 29 ] attack the other half of the equation, processing the GNSS signal to extract information about the GNSS carrier phase to improve the fused localization.…”
Section: Related Workmentioning
confidence: 99%
“…In this paper, we propose a novel feature extraction method based on variational autoencoders (VAEs) [18]. It is one of the popular models for unsupervised representation learning, and showed outstanding performance in feature learning [19,20]. It consists of a standard autoencoder component, and can approximate Bayesian inference for latent variable models.…”
Section: Introductionmentioning
confidence: 99%