Pose estimation method based on RNN-CNN for asteroid scene

LI Yuan; PENG Xiaodong; ZHOU Wugen; LI Yun; XIE Wenming

doi:10.13873/J.1000-9787(2020)08-0055-03

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Pose estimation method based on RNN-CNN for asteroid scene

更新时间：2025-09-11

- Pose estimation method based on RNN-CNN for asteroid scene
- Issue 8, Pages: 55-57+61(2020)
- 作者机构：
  
  1. 中国科学院国家空间科学中心
  2. 中国科学院大学计算机与控制学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2020)08-0055-03
  CLC： TP183;V476.4;TP391.41
- Published：2020
- 稿件说明：
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LI Yuan, PENG Xiaodong, ZHOU Wugen, et al. Pose estimation method based on RNN-CNN for asteroid scene[J]. 2020, (8): 55-57+61.
DOI：

LI Yuan, PENG Xiaodong, ZHOU Wugen, et al. Pose estimation method based on RNN-CNN for asteroid scene[J]. 2020, (8): 55-57+61. DOI： 10.13873/J.1000-9787(2020)08-0055-03.

摘要

针对小行星探测绕飞阶段采用视觉导航

小行星表面光照变化会影响视觉图像特征的稳定性而影响相对位姿估计的问题

提出了一个基于单应性矩阵的位姿估计方法。其采用一个基于循环神经网络—卷积神经网络(RNN-CNN)的深度学习框架估计单应性矩阵。实验结果表明:该网络提高了光照变化下单应性矩阵估计的精度。通过被用于小行星表面图像序列的位姿估计

证明了该方法的有效性及其在精度和效率方面优于传统方法。

Abstract

When using visual and other optical means to navigate during the asteroid exploration flight

the aspherical surface illumination changes will affect the stability of the visual image features

so as to affect the estimation of the relative pose. A pose estimation method based on homography matrix is proposed aiming at this problem. The method realizes estimation of the homography matrix with a deep learning framework based on a recurrent convolutional network-convolutional neural network（ RNN-CNN）. The experimental results show that the network improves the precision of the homography matrix estimation under illumination variation. The method is applied to estimate poses of the asteroid surface images sequence

which proves the effectiveness of the method and its superiority in precision and efficiency over traditional methods.

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