MotionHint: Self-Supervised Monocular Visual Odometry with Motion Constraints
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arXiv
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| Format: | Preprint |
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2021
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| author | Wang, Cong Wang, Yu-Ping Manocha, Dinesh |
| author_facet | Wang, Cong Wang, Yu-Ping Manocha, Dinesh |
| contents | We present a novel self-supervised algorithm named MotionHint for monocular visual odometry (VO) that takes motion constraints into account. A key aspect of our approach is to use an appropriate motion model that can help existing self-supervised monocular VO (SSM-VO) algorithms to overcome issues related to the local minima within their self-supervised loss functions. The motion model is expressed with a neural network named PPnet. It is trained to coarsely predict the next pose of the camera and the uncertainty of this prediction. Our self-supervised approach combines the original loss and the motion loss, which is the weighted difference between the prediction and the generated ego-motion. Taking two existing SSM-VO systems as our baseline, we evaluate our MotionHint algorithm on the standard KITTI benchmark. Experimental results show that our MotionHint algorithm can be easily applied to existing open-sourced state-of-the-art SSM-VO systems to greatly improve the performance by reducing the resulting ATE by up to 28.73%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2109_06768 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | MotionHint: Self-Supervised Monocular Visual Odometry with Motion Constraints Wang, Cong Wang, Yu-Ping Manocha, Dinesh Computer Vision and Pattern Recognition Robotics We present a novel self-supervised algorithm named MotionHint for monocular visual odometry (VO) that takes motion constraints into account. A key aspect of our approach is to use an appropriate motion model that can help existing self-supervised monocular VO (SSM-VO) algorithms to overcome issues related to the local minima within their self-supervised loss functions. The motion model is expressed with a neural network named PPnet. It is trained to coarsely predict the next pose of the camera and the uncertainty of this prediction. Our self-supervised approach combines the original loss and the motion loss, which is the weighted difference between the prediction and the generated ego-motion. Taking two existing SSM-VO systems as our baseline, we evaluate our MotionHint algorithm on the standard KITTI benchmark. Experimental results show that our MotionHint algorithm can be easily applied to existing open-sourced state-of-the-art SSM-VO systems to greatly improve the performance by reducing the resulting ATE by up to 28.73%. |
| title | MotionHint: Self-Supervised Monocular Visual Odometry with Motion Constraints |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2109.06768 |