TileTracker: Tracking Based Vector HD Mapping using Top-Down Road Images
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913571753951232 |
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| author | Mahdavian, Mohammad Chen, Mo Zhang, Yu |
| author_facet | Mahdavian, Mohammad Chen, Mo Zhang, Yu |
| contents | In this paper, we propose a tracking-based HD mapping algorithm for top-down road images, referred to as tile images. While HD maps traditionally rely on perspective camera images, our approach shows that tile images can also be effectively utilized, offering valuable contributions to this research area as it can be start of a new path in HD mapping algorithms. We modified the BEVFormer layers to generate BEV masks from tile images, which are then used by the model to generate divider and boundary lines. Our model was tested with both color and intensity images, and we present quantitative and qualitative results to demonstrate its performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02588 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TileTracker: Tracking Based Vector HD Mapping using Top-Down Road Images Mahdavian, Mohammad Chen, Mo Zhang, Yu Computer Vision and Pattern Recognition Machine Learning In this paper, we propose a tracking-based HD mapping algorithm for top-down road images, referred to as tile images. While HD maps traditionally rely on perspective camera images, our approach shows that tile images can also be effectively utilized, offering valuable contributions to this research area as it can be start of a new path in HD mapping algorithms. We modified the BEVFormer layers to generate BEV masks from tile images, which are then used by the model to generate divider and boundary lines. Our model was tested with both color and intensity images, and we present quantitative and qualitative results to demonstrate its performance. |
| title | TileTracker: Tracking Based Vector HD Mapping using Top-Down Road Images |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2411.02588 |