Satellite pose estimation method based on space carving and self-attention

LIU Jing-he; LIN Bao-jun

doi:10.37188/CJLCD.2023-0080

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Satellite pose estimation method based on space carving and self-attention

Image Processing | 更新时间：2024-07-28

- Satellite pose estimation method based on space carving and self-attention
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 12, Pages: 1736-1744(2023)
- 作者机构：
  
  1.中国科学院微小卫星创新研究院，上海 201203
  2.中国科学院大学，北京 100049
  3.中国科学院空天信息创新研究院，北京 100049
- 作者简介：
- 基金信息：
  
  Shanghai Science and Technology Development Funds(21yf1446000)
- DOI：10.37188/CJLCD.2023-0080
  CLC： TP391
- Received：01 March 2023，
  
  Revised：17 March 2023，
  
  Published：05 December 2023
- 稿件说明：
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LIU Jing-he, LIN Bao-jun. Satellite pose estimation method based on space carving and self-attention[J]. Chinese journal of liquid crystals and displays, 2023, 38(12): 1736-1744.
DOI：

LIU Jing-he, LIN Bao-jun. Satellite pose estimation method based on space carving and self-attention[J]. Chinese journal of liquid crystals and displays, 2023, 38(12): 1736-1744. DOI： 10.37188/CJLCD.2023-0080.

摘要

传统的单目姿态估计算法采用卷积网络在图像中定位若干关键点，再基于2D-3D匹配技术估计目标的姿态，但卫星上的关键点分布较分散，卷积网络由于其受限的感受野导致关键点的定位精度低，影响后续姿态估计的精度。此外传统流程需要人工标注关键点位置和目标的掩膜，标注成本高。为了解决传统方法感受野受限问题，在卷积网络中引入自注意力机制，赋予其全局建模能力，提高了关键点的定位精度。为了改善传统方法需要大量人工标注的问题，通过空间雕刻，重构了目标的点云，再将点云重投影回像素平面，自动化获取所需标签，省略了人工标注过程，提高了算法实用性。实验结果表明：所提算法在SPEED数据集上进行验证，关键点定位精度为92%，姿态平移误差为0.236%，姿态旋转误差为9.86

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2.20133328

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弧度，在简化算法复杂度的同时提升了精度。可以有效应用于航天器之间的相对姿态估计。

Abstract

In the traditional monocular pose estimation algorithm， convolution network is often used to locate several landmarks in the image， and then the target pose is estimated based on 2D-3D matching technology. But the distribution of landmarks on the satellite is scattered and due to the limited receptive field of convolution network， the positioning accuracy of landmarks is low， which affects the accuracy of subsequent pose estimation. In addition， the above process requires manual marking of landmark position labels and target mask labels， which is costly. For solving the two problems mentioned above， self-attention mechanism is introduced into the convolution network， which endows it with global modeling ability and improves the positioning accuracy of landmarks. In addition， the point cloud of the target is reconstructed through space carving， and then the point cloud is re-projected back to the pixel plane to automatically obtain the required labels， which improves the practicability of the algorithm. Experiment shows that the proposed algorithm has landmark localization accuracy of 92%， translation error of 0.236% and rotation error of 9.86×10

-3

rad on SPEED dataset， which improves the accuracy and simplifies the complexity. It can be effectively applied to relative pose estimation between spacecrafts.

关键词

Keywords

references

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