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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.17521 |
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| _version_ | 1866910716853747712 |
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| author | Sheng, Wenliang Zhao, Hongxu Chen, Lingpeng Zeng, Guangyang Shao, Yunling Hong, Yuze Yang, Chao Hong, Ziyang Wu, Junfeng |
| author_facet | Sheng, Wenliang Zhao, Hongxu Chen, Lingpeng Zeng, Guangyang Shao, Yunling Hong, Yuze Yang, Chao Hong, Ziyang Wu, Junfeng |
| contents | We consider the acoustic-n-point (AnP) problem, which estimates the pose of a 2D forward-looking sonar (FLS) according to n 3D-2D point correspondences. We explore the nature of the measured partial spherical coordinates and reveal their inherent relationships to translation and orientation. Based on this, we propose a bi-step efficient and statistically optimal AnP (BESTAnP) algorithm that decouples the estimation of translation and orientation. Specifically, in the first step, the translation estimation is formulated as the range-based localization problem based on distance-only measurements. In the second step, the rotation is estimated via eigendecomposition based on azimuth-only measurements and the estimated translation. BESTAnP is the first AnP algorithm that gives a closed-form solution for the full six-degree pose. In addition, we conduct bias elimination for BESTAnP such that it owns the statistical property of consistency. Through simulation and real-world experiments, we demonstrate that compared with the state-of-the-art (SOTA) methods, BESTAnP is over ten times faster and features real-time capacity in resource-constrained platforms while exhibiting comparable accuracy. Moreover, for the first time, we embed BESTAnP into a sonar-based odometry which shows its effectiveness for trajectory estimation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17521 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BESTAnP: Bi-Step Efficient and Statistically Optimal Estimator for Acoustic-n-Point Problem Sheng, Wenliang Zhao, Hongxu Chen, Lingpeng Zeng, Guangyang Shao, Yunling Hong, Yuze Yang, Chao Hong, Ziyang Wu, Junfeng Robotics We consider the acoustic-n-point (AnP) problem, which estimates the pose of a 2D forward-looking sonar (FLS) according to n 3D-2D point correspondences. We explore the nature of the measured partial spherical coordinates and reveal their inherent relationships to translation and orientation. Based on this, we propose a bi-step efficient and statistically optimal AnP (BESTAnP) algorithm that decouples the estimation of translation and orientation. Specifically, in the first step, the translation estimation is formulated as the range-based localization problem based on distance-only measurements. In the second step, the rotation is estimated via eigendecomposition based on azimuth-only measurements and the estimated translation. BESTAnP is the first AnP algorithm that gives a closed-form solution for the full six-degree pose. In addition, we conduct bias elimination for BESTAnP such that it owns the statistical property of consistency. Through simulation and real-world experiments, we demonstrate that compared with the state-of-the-art (SOTA) methods, BESTAnP is over ten times faster and features real-time capacity in resource-constrained platforms while exhibiting comparable accuracy. Moreover, for the first time, we embed BESTAnP into a sonar-based odometry which shows its effectiveness for trajectory estimation. |
| title | BESTAnP: Bi-Step Efficient and Statistically Optimal Estimator for Acoustic-n-Point Problem |
| topic | Robotics |
| url | https://arxiv.org/abs/2411.17521 |