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Award ID contains: 2006738

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  1. ; ; (, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))
  2. ; ; (, IEEE Embedded Systems Letters)
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  3. ; (, IEEE Transactions on Circuits and Systems II: Express Briefs)
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  4. ; ; ; ; (, Neurocomputing)
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  5. ; ; (, 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. 
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  6. ; ; (, 2022 IEEE International Symposium on Circuits and Systems (ISCAS))
  7. ; ; (, 2022 International Conference on Robotics and Automation (ICRA))
  8. ; ; ; (, IEEE Conference on Computer Vision and Pattern Recognition)
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  9. ; ; (, IEEE Winter Conference on Applications of Computer Vision)
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  10. ; ; (, IEEE Transactions on Circuits and Systems I: Regular Papers)
    null (Ed.)
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