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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Zusammenfassung: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.
ISSN:2643-2978
DOI:10.1109/ICIT63637.2025.10965253