Conditional Variational Autoencoders for Probabilistic Pose Regression
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909338673610752 |
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| author | Zangeneh, Fereidoon Bruns, Leonard Dekel, Amit Pieropan, Alessandro Jensfelt, Patric |
| author_facet | Zangeneh, Fereidoon Bruns, Leonard Dekel, Amit Pieropan, Alessandro Jensfelt, Patric |
| contents | Robots rely on visual relocalization to estimate their pose from camera images when they lose track. One of the challenges in visual relocalization is repetitive structures in the operation environment of the robot. This calls for probabilistic methods that support multiple hypotheses for robot's pose. We propose such a probabilistic method to predict the posterior distribution of camera poses given an observed image. Our proposed training strategy results in a generative model of camera poses given an image, which can be used to draw samples from the pose posterior distribution. Our method is streamlined and well-founded in theory and outperforms existing methods on localization in presence of ambiguities. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_04989 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Conditional Variational Autoencoders for Probabilistic Pose Regression Zangeneh, Fereidoon Bruns, Leonard Dekel, Amit Pieropan, Alessandro Jensfelt, Patric Computer Vision and Pattern Recognition Robots rely on visual relocalization to estimate their pose from camera images when they lose track. One of the challenges in visual relocalization is repetitive structures in the operation environment of the robot. This calls for probabilistic methods that support multiple hypotheses for robot's pose. We propose such a probabilistic method to predict the posterior distribution of camera poses given an observed image. Our proposed training strategy results in a generative model of camera poses given an image, which can be used to draw samples from the pose posterior distribution. Our method is streamlined and well-founded in theory and outperforms existing methods on localization in presence of ambiguities. |
| title | Conditional Variational Autoencoders for Probabilistic Pose Regression |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.04989 |