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-...

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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
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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
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  surname: Zhang
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  organization: College of Transportation, Shandong University of Science and Technology,Qingdao,China
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Snippet Accurate navigation and localization are essential for autonomous vehicles in complex environments. Visual place recognition (VPR) provides an efficient and...
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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
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