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Lightweight, Viewpoint-Invariant Visual Place Recognition in Changing Environments
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS MRO Lab)ORCID-id: 0000-0003-3788-499X
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS MRO Lab)ORCID-id: 0000-0002-2953-1564
2018 (engelsk)Inngår i: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 3, nr 2, s. 957-964Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper presents a viewpoint-invariant place recognition algorithm which is robust to changing environments while requiring only a small memory footprint. It demonstrates that condition-invariant local features can be combined with Vectors of Locally Aggregated Descriptors (VLAD) to reduce high-dimensional representations of images to compact binary signatures while retaining place matching capability across visually dissimilar conditions. This system provides a speed-up of two orders of magnitude over direct feature matching, and outperforms a bag-of-visual-words approach with near-identical computation speed and memory footprint. The experimental results show that single-image place matching from non-aligned images can be achieved in visually changing environments with as few as 256 bits (32 bytes) per image.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 3, nr 2, s. 957-964
Emneord [en]
Visual-based navigation, recognition, localization
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Datavetenskap
Identifikatorer
URN: urn:nbn:se:oru:diva-64652DOI: 10.1109/LRA.2018.2793308ISI: 000424646100015OAI: oai:DiVA.org:oru-64652DiVA, id: diva2:1178647
Merknad

Funding Agency:

Semantic Robots Research Profile - Swedish Knowledge Foundation

Tilgjengelig fra: 2018-01-30 Laget: 2018-01-30 Sist oppdatert: 2018-02-28bibliografisk kontrollert

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Lowry, StephanieAndreasson, Henrik

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