dc.contributor.author |
Kim, Jonathan JY |
|
dc.contributor.author |
Urschler, Martin |
|
dc.contributor.author |
Riddle, Patricia J |
|
dc.contributor.author |
Wicker, Jörg S |
|
dc.coverage.spatial |
ELECTR NETWORK |
|
dc.date.accessioned |
2022-09-12T02:26:59Z |
|
dc.date.available |
2022-09-12T02:26:59Z |
|
dc.date.issued |
2021-10-21 |
|
dc.identifier.citation |
(2021). Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems, 00, 5425-5425. |
|
dc.identifier.isbn |
9781665417143 |
|
dc.identifier.issn |
2153-0858 |
|
dc.identifier.uri |
https://hdl.handle.net/2292/61130 |
|
dc.description.abstract |
Loop closure detection is an essential tool of Simultaneous Localization and Mapping (SLAM) to minimize drift in its localization. Many state-of-the-art loop closure detection (LCD) algorithms use visual Bag-of-Words (vBoW), which is robust against partial occlusions in a scene but cannot perceive the semantics or spatial relationships between feature points. CNN object extraction can address those issues, by providing semantic labels and spatial relationships between objects in a scene. Previous work has mainly focused on replacing vBoW with CNN derived features. In this paper we propose SymbioLCD, a novel ensemble-based LCD that utilizes both CNN-extracted objects and vBoW features for LCD candidate prediction. When used in tandem, the added elements of object semantics and spatial-awareness creates a more robust and symbiotic loop closure detection system. The proposed SymbioLCD uses scale-invariant spatial and semantic matching, Hausdorff distance with temporal constraints, and a Random Forest that utilizes combined information from both CNN-extracted objects and vBoW features for predicting accurate loop closure candidates. Evaluation of the proposed method shows it outperforms other Machine Learning (ML) algorithms-such as SVM, Decision Tree and Neural Network, and demonstrates that there is a strong symbiosis between CNN-extracted object information and vBoW features which assists accurate LCD candidate prediction. Furthermore, it is able to perceive loop closure candidates earlier than state-of-the-art SLAM algorithms, utilizing added spatial and semantic information from CNN-extracted objects. |
|
dc.publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
|
dc.relation.ispartof |
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
|
dc.relation.ispartofseries |
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.subject |
Science & Technology |
|
dc.subject |
Technology |
|
dc.subject |
Automation & Control Systems |
|
dc.subject |
Computer Science, Artificial Intelligence |
|
dc.subject |
Engineering, Electrical & Electronic |
|
dc.subject |
Robotics |
|
dc.subject |
Computer Science |
|
dc.subject |
Engineering |
|
dc.subject |
cs.CV |
|
dc.subject |
cs.RO |
|
dc.title |
SymbioLCD: Ensemble-Based Loop Closure Detection using CNN-Extracted
Objects and Visual Bag-of-Words |
|
dc.type |
Conference Item |
|
dc.identifier.doi |
10.1109/iros51168.2021.9636622 |
|
pubs.begin-page |
5425 |
|
pubs.volume |
00 |
|
dc.date.updated |
2022-08-02T21:11:46Z |
|
dc.rights.holder |
Copyright: The authors |
en |
pubs.author-url |
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000755125504046&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e41486220adb198d0efde5a3b153e7d |
|
pubs.end-page |
5425 |
|
pubs.finish-date |
2021-10-01 |
|
pubs.publication-status |
Published |
|
pubs.start-date |
2021-09-27 |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RestrictedAccess |
en |
pubs.subtype |
Conference Paper |
|
pubs.elements-id |
862259 |
|
pubs.org-id |
Science |
|
pubs.org-id |
School of Computer Science |
|
pubs.arxiv-id |
arXiv:2110.11491 (arxiv) |
|
dc.identifier.eissn |
2153-0866 |
|
pubs.record-created-at-source-date |
2022-08-03 |
|
pubs.online-publication-date |
2021-10-01 |
|