DH-PTAM: A Deep Hybrid Stereo Events-Frames Parallel Tracking And Mapping System
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909219969564672 |
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| author | Soliman, Abanob Bonardi, Fabien Sidibé, Désiré Bouchafa, Samia |
| author_facet | Soliman, Abanob Bonardi, Fabien Sidibé, Désiré Bouchafa, Samia |
| contents | This paper presents a robust approach for a visual parallel tracking and mapping (PTAM) system that excels in challenging environments. Our proposed method combines the strengths of heterogeneous multi-modal visual sensors, including stereo event-based and frame-based sensors, in a unified reference frame through a novel spatio-temporal synchronization of stereo visual frames and stereo event streams. We employ deep learning-based feature extraction and description for estimation to enhance robustness further. We also introduce an end-to-end parallel tracking and mapping optimization layer complemented by a simple loop-closure algorithm for efficient SLAM behavior. Through comprehensive experiments on both small-scale and large-scale real-world sequences of VECtor and TUM-VIE benchmarks, our proposed method (DH-PTAM) demonstrates superior performance in terms of robustness and accuracy in adverse conditions, especially in large-scale HDR scenarios. Our implementation's research-based Python API is publicly available on GitHub for further research and development: https://github.com/AbanobSoliman/DH-PTAM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_01891 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DH-PTAM: A Deep Hybrid Stereo Events-Frames Parallel Tracking And Mapping System Soliman, Abanob Bonardi, Fabien Sidibé, Désiré Bouchafa, Samia Computer Vision and Pattern Recognition Robotics Image and Video Processing Signal Processing This paper presents a robust approach for a visual parallel tracking and mapping (PTAM) system that excels in challenging environments. Our proposed method combines the strengths of heterogeneous multi-modal visual sensors, including stereo event-based and frame-based sensors, in a unified reference frame through a novel spatio-temporal synchronization of stereo visual frames and stereo event streams. We employ deep learning-based feature extraction and description for estimation to enhance robustness further. We also introduce an end-to-end parallel tracking and mapping optimization layer complemented by a simple loop-closure algorithm for efficient SLAM behavior. Through comprehensive experiments on both small-scale and large-scale real-world sequences of VECtor and TUM-VIE benchmarks, our proposed method (DH-PTAM) demonstrates superior performance in terms of robustness and accuracy in adverse conditions, especially in large-scale HDR scenarios. Our implementation's research-based Python API is publicly available on GitHub for further research and development: https://github.com/AbanobSoliman/DH-PTAM. |
| title | DH-PTAM: A Deep Hybrid Stereo Events-Frames Parallel Tracking And Mapping System |
| topic | Computer Vision and Pattern Recognition Robotics Image and Video Processing Signal Processing |
| url | https://arxiv.org/abs/2306.01891 |