Asynchronous Event-Inertial Odometry using a Unified Gaussian Process Regression Framework
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913631299436544 |
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| author | Li, Xudong Wang, Zhixiang Liu, Zihao Zhang, Yizhai Zhang, Fan Yao, Xiuming Huang, Panfeng |
| author_facet | Li, Xudong Wang, Zhixiang Liu, Zihao Zhang, Yizhai Zhang, Fan Yao, Xiuming Huang, Panfeng |
| contents | Recent works have combined monocular event camera and inertial measurement unit to estimate the $SE(3)$ trajectory. However, the asynchronicity of event cameras brings a great challenge to conventional fusion algorithms. In this paper, we present an asynchronous event-inertial odometry under a unified Gaussian Process (GP) regression framework to naturally fuse asynchronous data associations and inertial measurements. A GP latent variable model is leveraged to build data-driven motion prior and acquire the analytical integration capacity. Then, asynchronous event-based feature associations and integral pseudo measurements are tightly coupled using the same GP framework. Subsequently, this fusion estimation problem is solved by underlying factor graph in a sliding-window manner. With consideration of sparsity, those historical states are marginalized orderly. A twin system is also designed for comparison, where the traditional inertial preintegration scheme is embedded in the GP-based framework to replace the GP latent variable model. Evaluations on public event-inertial datasets demonstrate the validity of both systems. Comparison experiments show competitive precision compared to the state-of-the-art synchronous scheme. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03136 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Asynchronous Event-Inertial Odometry using a Unified Gaussian Process Regression Framework Li, Xudong Wang, Zhixiang Liu, Zihao Zhang, Yizhai Zhang, Fan Yao, Xiuming Huang, Panfeng Robotics Recent works have combined monocular event camera and inertial measurement unit to estimate the $SE(3)$ trajectory. However, the asynchronicity of event cameras brings a great challenge to conventional fusion algorithms. In this paper, we present an asynchronous event-inertial odometry under a unified Gaussian Process (GP) regression framework to naturally fuse asynchronous data associations and inertial measurements. A GP latent variable model is leveraged to build data-driven motion prior and acquire the analytical integration capacity. Then, asynchronous event-based feature associations and integral pseudo measurements are tightly coupled using the same GP framework. Subsequently, this fusion estimation problem is solved by underlying factor graph in a sliding-window manner. With consideration of sparsity, those historical states are marginalized orderly. A twin system is also designed for comparison, where the traditional inertial preintegration scheme is embedded in the GP-based framework to replace the GP latent variable model. Evaluations on public event-inertial datasets demonstrate the validity of both systems. Comparison experiments show competitive precision compared to the state-of-the-art synchronous scheme. |
| title | Asynchronous Event-Inertial Odometry using a Unified Gaussian Process Regression Framework |
| topic | Robotics |
| url | https://arxiv.org/abs/2412.03136 |