南京航空航天大学学报（英文版）2024，Vol.41Issue(1)：122-134,13.DOI:10.16356/j.1005-1120.2024.01.010
DMANet:针对空间非合作目标位姿估计的密集多尺度注意力网络
DMANet:Dense Multi-scale Attention Network for Space Non-cooperative Object Pose Estimation
张钊 1胡瑀晖 1周栋 1吴立刚 1姚蔚然 1李鹏1
作者信息
- 1. 哈尔滨工业大学航天学院,150001 哈尔滨,中国
- 折叠
摘要
Abstract
Accurate pose estimation of space non-cooperative targets with a monocular camera is crucial to space debris removal,autonomous rendezvous,and other on-orbit services.However,monocular pose estimation methods lack depth information,resulting in scale uncertainty issue that significantly reduces their accuracy and real-time performance.We first propose a multi-scale attention block(MAB)to extract complex high-dimensional semantic features from the input image.Second,based on the MAB module,we propose a dense multi-scale attention network(DMANet)for estimating the 6-degree-of-freedom(DoF)pose of space non-cooperative targets,which consists of planar position estimation,depth position estimation,and attitude estimation branches.By introducing an Euler angle-based soft classification method,we formulate the pose regression problem as a classical classification problem.Besides,we design a space non-cooperative object model and construct a pose estimation dataset by using Coppeliasim.Finally,we thoroughly evaluate the proposed method on the SPEED+,URSO datasets and our dataset,compared to other state-of-the-art methods.Experiment results demonstrate that the DMANet achieves excellent pose estimation accuracy.关键词
六自由度位姿估计/空间非合作目标/多尺度注意力机制/深度学习/神经网络Key words
6-degree-of-freedom(DoF)pose estimation/space non-cooperative object/multi-scale attention/deep learning/neural network分类
信息技术与安全科学引用本文复制引用
张钊,胡瑀晖,周栋,吴立刚,姚蔚然,李鹏..DMANet:针对空间非合作目标位姿估计的密集多尺度注意力网络[J].南京航空航天大学学报（英文版）,2024,41(1):122-134,13.
