TR2020-064

Collaborative Localization Based on Traffic Landmarks for Autonomous Driving


    •  Chen, S., Zhang, N., Sun, H., "Collaborative Localization Based on Traffic Landmarks for Autonomous Driving", IEEE International Symposium on Circuits and Systems (ISCAS), May 2020.
      BibTeX TR2020-064 PDF
      • @inproceedings{Chen2020may,
      • author = {Chen, Siheng and Zhang, Ningxiao and Sun, Huifang},
      • title = {Collaborative Localization Based on Traffic Landmarks for Autonomous Driving},
      • booktitle = {IEEE International Symposium on Circuits and Systems (ISCAS)},
      • year = 2020,
      • month = may,
      • url = {https://www.merl.com/publications/TR2020-064}
      • }
  • MERL Contacts:
  • Research Areas:

    Computer Vision, Signal Processing

Localizing an autonomous vehicle in real-time is critical for robust autonomous driving. As a standard approach, the mapbased localization is robust and fast; however, it is expensive to create and maintain a large-scale high-definition map. In this paper, we propose an online localization technique based on the vehicle-to-vehicle communication and traffic landmark detection; called collaborative localization. This can potentially serve as a new complement to the standard localization solutions. We theoretically show that multiple vehicles with multiple traffic landmarks would significantly improve the localization performance. We then propose a practical algorithm, which leverages graph matching to handle practical issues, such as traffic landmark association. The experimental results validate the potential of the proposed methods.