Highly Robust Visual Place Recognition Through Spatial Matching of CNN Features
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F20%3A00348828" target="_blank" >RIV/68407700:21730/20:00348828 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICRA40945.2020.9196967" target="_blank" >https://doi.org/10.1109/ICRA40945.2020.9196967</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICRA40945.2020.9196967" target="_blank" >10.1109/ICRA40945.2020.9196967</a>
Alternative languages
Result language
angličtina
Original language name
Highly Robust Visual Place Recognition Through Spatial Matching of CNN Features
Original language description
We revise, improve and extend the system previously introduced by us and named SSM-VPR (Semantic and Spatial Matching Visual Place Recognition), largely boosting its performance above the current state of the art. The system encodes images of places by employing the activations of different layers of a pre-trained, off-the-shelf, VGG16 Convolutional Neural Network (CNN) architecture. It consists of two stages: given a query image of a place, (1) a list of candidates is selected from a database of places and (2) the candidates are geometrically compared with the query. The comparison is made by matching CNN features and, equally important, their spatial locations, selecting the best candidate as the recognized place. The performance of the system is maximized by finding optimal image resolutions during the second stage and by exploiting temporal correlation between consecutive frames in the employed datasets.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20204 - Robotics and automatic control
Result continuities
Project
<a href="/en/project/EF15_003%2F0000470" target="_blank" >EF15_003/0000470: Robotics 4 Industry 4.0</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IEEE International Conference on Robotics and Automation (ICRA)
ISBN
978-1-7281-7395-5
ISSN
1050-4729
e-ISSN
2577-087X
Number of pages
8
Pages from-to
3748-3755
Publisher name
IEEE Xplore
Place of publication
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Event location
Paris
Event date
May 31, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000712319502086