CodedVO: Coded Visual Odometry
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916336338206720 |
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| author | Shah, Sachin Rajyaguru, Naitri Singh, Chahat Deep Metzler, Christopher Aloimonos, Yiannis |
| author_facet | Shah, Sachin Rajyaguru, Naitri Singh, Chahat Deep Metzler, Christopher Aloimonos, Yiannis |
| contents | Autonomous robots often rely on monocular cameras for odometry estimation and navigation. However, the scale ambiguity problem presents a critical barrier to effective monocular visual odometry. In this paper, we present CodedVO, a novel monocular visual odometry method that overcomes the scale ambiguity problem by employing custom optics to physically encode metric depth information into imagery. By incorporating this information into our odometry pipeline, we achieve state-of-the-art performance in monocular visual odometry with a known scale. We evaluate our method in diverse indoor environments and demonstrate its robustness and adaptability. We achieve a 0.08m average trajectory error in odometry evaluation on the ICL-NUIM indoor odometry dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18240 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CodedVO: Coded Visual Odometry Shah, Sachin Rajyaguru, Naitri Singh, Chahat Deep Metzler, Christopher Aloimonos, Yiannis Robotics Computer Vision and Pattern Recognition Autonomous robots often rely on monocular cameras for odometry estimation and navigation. However, the scale ambiguity problem presents a critical barrier to effective monocular visual odometry. In this paper, we present CodedVO, a novel monocular visual odometry method that overcomes the scale ambiguity problem by employing custom optics to physically encode metric depth information into imagery. By incorporating this information into our odometry pipeline, we achieve state-of-the-art performance in monocular visual odometry with a known scale. We evaluate our method in diverse indoor environments and demonstrate its robustness and adaptability. We achieve a 0.08m average trajectory error in odometry evaluation on the ICL-NUIM indoor odometry dataset. |
| title | CodedVO: Coded Visual Odometry |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.18240 |