UNO: Unified Self-Supervised Monocular Odometry for Platform-Agnostic Deployment
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915333043912704 |
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| author | Zhao, Wentao Niu, Yihe Wang, Yanbo Deng, Tianchen Yuan, Shenghai Wang, Zhenli Guo, Rui Wang, Jingchuan |
| author_facet | Zhao, Wentao Niu, Yihe Wang, Yanbo Deng, Tianchen Yuan, Shenghai Wang, Zhenli Guo, Rui Wang, Jingchuan |
| contents | This work presents UNO, a unified monocular visual odometry framework that enables robust and adaptable pose estimation across diverse environments, platforms, and motion patterns. Unlike traditional methods that rely on deployment-specific tuning or predefined motion priors, our approach generalizes effectively across a wide range of real-world scenarios, including autonomous vehicles, aerial drones, mobile robots, and handheld devices. To this end, we introduce a Mixture-of-Experts strategy for local state estimation, with several specialized decoders that each handle a distinct class of ego-motion patterns. Moreover, we introduce a fully differentiable Gumbel-Softmax module that constructs a robust inter-frame correlation graph, selects the optimal expert decoder, and prunes erroneous estimates. These cues are then fed into a unified back-end that combines pre-trained, scale-independent depth priors with a lightweight bundling adjustment to enforce geometric consistency. We extensively evaluate our method on three major benchmark datasets: KITTI (outdoor/autonomous driving), EuRoC-MAV (indoor/aerial drones), and TUM-RGBD (indoor/handheld), demonstrating state-of-the-art performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07013 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UNO: Unified Self-Supervised Monocular Odometry for Platform-Agnostic Deployment Zhao, Wentao Niu, Yihe Wang, Yanbo Deng, Tianchen Yuan, Shenghai Wang, Zhenli Guo, Rui Wang, Jingchuan Computer Vision and Pattern Recognition This work presents UNO, a unified monocular visual odometry framework that enables robust and adaptable pose estimation across diverse environments, platforms, and motion patterns. Unlike traditional methods that rely on deployment-specific tuning or predefined motion priors, our approach generalizes effectively across a wide range of real-world scenarios, including autonomous vehicles, aerial drones, mobile robots, and handheld devices. To this end, we introduce a Mixture-of-Experts strategy for local state estimation, with several specialized decoders that each handle a distinct class of ego-motion patterns. Moreover, we introduce a fully differentiable Gumbel-Softmax module that constructs a robust inter-frame correlation graph, selects the optimal expert decoder, and prunes erroneous estimates. These cues are then fed into a unified back-end that combines pre-trained, scale-independent depth priors with a lightweight bundling adjustment to enforce geometric consistency. We extensively evaluate our method on three major benchmark datasets: KITTI (outdoor/autonomous driving), EuRoC-MAV (indoor/aerial drones), and TUM-RGBD (indoor/handheld), demonstrating state-of-the-art performance. |
| title | UNO: Unified Self-Supervised Monocular Odometry for Platform-Agnostic Deployment |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.07013 |