PRFusion: Toward Effective and Robust Multi-Modal Place Recognition With Image and Point Cloud Fusion

Place recognition plays a crucial role in the fields of robotics and computer vision, finding applications in areas such as autonomous driving, mapping, and localization. Place recognition identifies a place using query sensor data and a known database. One of the main challenges is to develop a model that can deliver accurate results while being robust to environmental variations. The authors propose two multi-modal place recognition models, namely PRFusion and PRFusion++. PRFusion utilizes global fusion with manifold metric attention, enabling effective interaction between features without requiring camera-LiDAR extrinsic calibrations. In contrast, PRFusion++ assumes the availability of extrinsic calibrations and leverages pixel-point correspondences to enhance feature learning on local windows. Additionally, both models incorporate neural diffusion layers, which enable reliable operation even in challenging environments. They verify the state-of-the-art performance of both models on three large-scale benchmarks. Notably, they outperform existing models by a substantial margin of +3.0 AR@1 on the demanding Boreas dataset. Furthermore, they conduct ablation studies to validate the effectiveness of their proposed methods.

• Record URL:
  • https://doi.org/10.1109/TITS.2024.3465830
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2024, IEEE.
• Authors:
  • Wang, Sijie
  • Kang, Qiyu
  • She, Rui
  • Zhao, Kai
  • Song, Yang
  • Tay, Wee Peng
• Publication Date: 2024-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 20523-20534
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 25
  • Issue Number: 12
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Cameras; Computer vision; Data fusion; Laser radar; Neural networks; Point clouds
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01957056
• Record Type: Publication
• Files: TRIS
• Created Date: May 31 2025 3:16PM