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Creators/Authors contains: "Zhang, Ziming"

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  1. ; ; ; ; ( , Neurocomputing)
    Free, publicly-accessible full text available October 1, 2023
  2. ; ; ( , Sensors)
    Cross-modal vehicle localization is an important task for automated driving systems. This research proposes a novel approach based on LiDAR point clouds and OpenStreetMaps (OSM) via a constrained particle filter, which significantly improves the vehicle localization accuracy. The OSM modality provides not only a platform to generate simulated point cloud images, but also geometrical constraints (e.g., roads) to improve the particle filter’s final result. The proposed approach is deterministic without any learning component or need for labelled data. Evaluated by using the KITTI dataset, it achieves accurate vehicle pose tracking with a position error of less than 3 m when considering the mean error across all the sequences. This method shows state-of-the-art accuracy when compared with the existing methods based on OSM or satellite maps.
    Free, publicly-accessible full text available July 1, 2023
  3. ; ; ( , IEEE Winter Conference on Applications of Computer Vision)
    Full Text Available 
  4. ; ; ; ( , IEEE Conference on Computer Vision and Pattern Recognition)
    Full Text Available 
  5. ; ; ; ; ( , Advances in Neural Information Processing (NeurIPS 2021))
    Accepted Manuscript Full Text Available
  6. ; ; ; ; ( , The 60th IEEE Conference on Decision and Control (CDC 2021))
    Full Text Available 
  7. ; ; ; ; ( , 2020 IEEE International Conference on Data Mining (ICDM))
    Full Text Available 
  8. ; ; ; ; ; ; ( , IEEE International Conference on Data Mining (ICDM))
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  9. ; ; ( , IEEE Transactions on Pattern Analysis and Machine Intelligence)
    Full Text Available 
  10. ; ; ( , Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems)
    Accepted Manuscript Full Text Available