BEV-ODOM: Reducing Scale Drift in Monocular Visual Odometry with BEV Representation
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913578804576256 |
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| author | Wei, Yufei Lu, Sha Han, Fuzhang Xiong, Rong Wang, Yue |
| author_facet | Wei, Yufei Lu, Sha Han, Fuzhang Xiong, Rong Wang, Yue |
| contents | Monocular visual odometry (MVO) is vital in autonomous navigation and robotics, providing a cost-effective and flexible motion tracking solution, but the inherent scale ambiguity in monocular setups often leads to cumulative errors over time. In this paper, we present BEV-ODOM, a novel MVO framework leveraging the Bird's Eye View (BEV) Representation to address scale drift. Unlike existing approaches, BEV-ODOM integrates a depth-based perspective-view (PV) to BEV encoder, a correlation feature extraction neck, and a CNN-MLP-based decoder, enabling it to estimate motion across three degrees of freedom without the need for depth supervision or complex optimization techniques. Our framework reduces scale drift in long-term sequences and achieves accurate motion estimation across various datasets, including NCLT, Oxford, and KITTI. The results indicate that BEV-ODOM outperforms current MVO methods, demonstrating reduced scale drift and higher accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10195 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BEV-ODOM: Reducing Scale Drift in Monocular Visual Odometry with BEV Representation Wei, Yufei Lu, Sha Han, Fuzhang Xiong, Rong Wang, Yue Robotics Monocular visual odometry (MVO) is vital in autonomous navigation and robotics, providing a cost-effective and flexible motion tracking solution, but the inherent scale ambiguity in monocular setups often leads to cumulative errors over time. In this paper, we present BEV-ODOM, a novel MVO framework leveraging the Bird's Eye View (BEV) Representation to address scale drift. Unlike existing approaches, BEV-ODOM integrates a depth-based perspective-view (PV) to BEV encoder, a correlation feature extraction neck, and a CNN-MLP-based decoder, enabling it to estimate motion across three degrees of freedom without the need for depth supervision or complex optimization techniques. Our framework reduces scale drift in long-term sequences and achieves accurate motion estimation across various datasets, including NCLT, Oxford, and KITTI. The results indicate that BEV-ODOM outperforms current MVO methods, demonstrating reduced scale drift and higher accuracy. |
| title | BEV-ODOM: Reducing Scale Drift in Monocular Visual Odometry with BEV Representation |
| topic | Robotics |
| url | https://arxiv.org/abs/2411.10195 |