MDPR-Net: Dynamic Target Interference Removal and Autonomous Vehicle Place Recognition Network for Multi-View Images

Accurate navigation and localization are essential for autonomous vehicles in complex environments. Visual place recognition (VPR) provides an efficient and cost-effective method for environmental representation. Our study introduces MDPR-Net, an autonomous vehicle positioning network utilizing 360-...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE International Conference on Industrial Technology (Online) S. 1 - 6
Hauptverfasser: Zhang, Shuo, Li, Zhongzheng, Sun, Xiaoyu, Zhao, Fenglei, Kong, Dong, Zhang, Liye
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 26.03.2025
Schlagworte:
Autonomous vehicles
Context modeling
Dynamic targets filtering
Filters
Image recognition
Interference
Location awareness
Navigation
Target recognition
Vehicle dynamics
Visual place recognition
ISSN:2643-2978
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Abstract Accurate navigation and localization are essential for autonomous vehicles in complex environments. Visual place recognition (VPR) provides an efficient and cost-effective method for environmental representation. Our study introduces MDPR-Net, an autonomous vehicle positioning network utilizing 360-degree images. The dynamic interference removal module (DIR) eliminates dynamic targets filtering, ensuring precise environmental perception. Following DIR, a multi-view image encoder module (MIE) encodes the filtered panoramic images with shared weights, capturing comprehensive features. The image-relation attention module (IRA) then associates these features across multi-view images, enhancing the model's ability to understand the scene contextually. This approach is demonstrated on the nuScenes dataset, yielding promising results.
AbstractList Accurate navigation and localization are essential for autonomous vehicles in complex environments. Visual place recognition (VPR) provides an efficient and cost-effective method for environmental representation. Our study introduces MDPR-Net, an autonomous vehicle positioning network utilizing 360-degree images. The dynamic interference removal module (DIR) eliminates dynamic targets filtering, ensuring precise environmental perception. Following DIR, a multi-view image encoder module (MIE) encodes the filtered panoramic images with shared weights, capturing comprehensive features. The image-relation attention module (IRA) then associates these features across multi-view images, enhancing the model's ability to understand the scene contextually. This approach is demonstrated on the nuScenes dataset, yielding promising results.
Author Zhao, Fenglei
Li, Zhongzheng
Kong, Dong
Zhang, Shuo
Sun, Xiaoyu
Zhang, Liye
Author_xml – sequence: 1
  givenname: Shuo
  surname: Zhang
  fullname: Zhang, Shuo
  email: shuo0028@sdust.edu.cn
  organization: College of Transportation, Shandong University of Science and Technology,Qingdao,China
– sequence: 2
  givenname: Zhongzheng
  orcidid: 0009-0005-2172-5283
  surname: Li
  fullname: Li, Zhongzheng
  organization: College of Transportation, Shandong University of Science and Technology,Qingdao,China
– sequence: 3
  givenname: Xiaoyu
  orcidid: 0009-0005-6934-6485
  surname: Sun
  fullname: Sun, Xiaoyu
  organization: College of Transportation, Shandong University of Science and Technology,Qingdao,China
– sequence: 4
  givenname: Fenglei
  orcidid: 0009-0009-0469-2693
  surname: Zhao
  fullname: Zhao, Fenglei
  organization: College of Transportation, Shandong University of Science and Technology,Qingdao,China
– sequence: 5
  givenname: Dong
  orcidid: 0000-0002-1864-423X
  surname: Kong
  fullname: Kong, Dong
  organization: College of Transportation, Shandong University of Science and Technology,Qingdao,China
– sequence: 6
  givenname: Liye
  orcidid: 0000-0003-0965-2374
  surname: Zhang
  fullname: Zhang, Liye
  organization: College of Transportation, Shandong University of Science and Technology,Qingdao,China
BookMark eNo1kF1LwzAYhaMoOOf-gWD-QGeaN0kb78bmR2HTMcpuR0zfzmibSJs59u8dflwdeDg8cM4lOfPBIyE3KRunKdO3xbQoFSjIxpxxOT4iJbmEEzLSmc4BUslTLdkpGXAlIOE6yy_IqO_fGWPAGQihBiQuZstV8ozxjs4O3rTO0tJ0W4y08BG7Gjv0FukK2_BlGmp8RSe7GHxow66na3xztkG6bMxPyYatd9EFT4_Gfeg-aB06utg10SVrh3tatGaL_RU5r03T4-gvh6R8uC-nT8n85bGYTuaJ0xATjags57Wyr6kQUleguBEC7HEnZCgtZkZYTKvc5qxGkLkRTB2RrjKhkcOQXP9qHSJuPjvXmu6w-f8JvgFWIV9-
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/ICIT63637.2025.10965253
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISBN 9798331521950
EISSN 2643-2978
EndPage 6
ExternalDocumentID 10965253
Genre orig-research
GroupedDBID 6IE
6IF
6IL
6IN
AAJGR
AAWTH
ABLEC
ADZIZ
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
CHZPO
IEGSK
OCL
RIE
RIL
ID FETCH-LOGICAL-i93t-9ee6c22f6cb14459d362a443c96537e5ce7a4ce1d8c80fe358a406a4c9d749e23
IEDL.DBID RIE
IngestDate Wed Apr 30 05:50:36 EDT 2025
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i93t-9ee6c22f6cb14459d362a443c96537e5ce7a4ce1d8c80fe358a406a4c9d749e23
ORCID 0000-0002-1864-423X
0000-0003-0965-2374
0009-0005-6934-6485
0009-0005-2172-5283
0009-0009-0469-2693
PageCount 6
ParticipantIDs ieee_primary_10965253
PublicationCentury 2000
PublicationDate 2025-March-26
PublicationDateYYYYMMDD 2025-03-26
PublicationDate_xml – month: 03
  year: 2025
  text: 2025-March-26
  day: 26
PublicationDecade 2020
PublicationTitle IEEE International Conference on Industrial Technology (Online)
PublicationTitleAbbrev ICIT
PublicationYear 2025
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0003203446
Score 1.9033605
Snippet Accurate navigation and localization are essential for autonomous vehicles in complex environments. Visual place recognition (VPR) provides an efficient and...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms Autonomous vehicles
Context modeling
Dynamic targets filtering
Filters
Image recognition
Interference
Location awareness
Navigation
Target recognition
Vehicle dynamics
Visual place recognition
Title MDPR-Net: Dynamic Target Interference Removal and Autonomous Vehicle Place Recognition Network for Multi-View Images
URI https://ieeexplore.ieee.org/document/10965253
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwELZoxQALryLe8sCa0thJHLOhlooORFEVVd0qx76IDk1Rm8Lf5-w0BQYGtshKosR3vvOdv--OkPtQa6aEUV6R43ILQCgvlr7yOLAc1UXx2BjXbEIkSTydynRLVndcGABw4DPo2kt3lm-WemNTZbjCZRSykLdISwhRk7V2CRXObPW6aIvhwlsfRv1RFvGICwwDWdhtnv7VR8W5keHRPz_gmHS-CXk03bmaE7IH5Sk5_FFL8IxUr4N07CVQPdJB3WWeZg7lTV3Sr3nLGBZLVC6qSkOfNpWlNGDsTyfwZhWIpjarTscNqmhZ0qTGiVPc3FLH1vUmc_ikowUaonWHZMPnrP_ibVsqeHPJK08CRJqxItI5BlKhNOi-VBBwjb_FBYQaZRVo8E2s414BPIwVOnwckkYEEhg_J-1yWcIFoYGvcl70fFXEAdoBDKQDzXK0EL7CHYoxl6Rj52_2XhfNmDVTd_XH-DU5sFKy8C4W3ZB2tdrALdnXH9V8vbpzov4CZkyqzg
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8MwDI5gIAEXXkO8yYFrx5qkL25oMK1iq6apmnab0sQVO6xFWwd_Hyd7AAcO3KqordrYsWPn-2xC7j2lmAy0dPIMl5uAQDph5EqHA8tQXSQPtbbNJoIkCUejqL8iq1suDABY8Bk0zKU9y9elWphUGa7wyPeYx7fJjicEc5d0rU1KhTNTv85fobjw5oe4Fac-93mAgSDzGuvnf3VSsY6kffjPTzgi9W9KHu1vnM0x2YLihBz8qCZ4Sqrec3_gJFA90udln3maWpw3tWm_9VsGMC1RvagsNH1aVIbUgNE_HcKbUSHaN3l1OljjisqCJkukOMXtLbV8XWc4gU8aT9EUzeskbb-krY6zaqrgTCJeORGArxjLfZVhKOVFGh2YFIIr_C0egKdQWkKBq0MVNnPgXijR5eNQpAMRAeNnpFaUBZwTKlyZ8bzpyjwUaAkwlBaKZWgjXIl7FK0vSN3M3_h9WTZjvJ66yz_G78heJ-11x904eb0i-0ZiBuzF_GtSq2YLuCG76qOazGe3VuxfdjSuFQ
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=IEEE+International+Conference+on+Industrial+Technology+%28Online%29&rft.atitle=MDPR-Net%3A+Dynamic+Target+Interference+Removal+and+Autonomous+Vehicle+Place+Recognition+Network+for+Multi-View+Images&rft.au=Zhang%2C+Shuo&rft.au=Li%2C+Zhongzheng&rft.au=Sun%2C+Xiaoyu&rft.au=Zhao%2C+Fenglei&rft.date=2025-03-26&rft.pub=IEEE&rft.eissn=2643-2978&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2FICIT63637.2025.10965253&rft.externalDocID=10965253