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- Title
DMANet: Dense Multi‑scale Attention Network for Space Non‑cooperative Object Pose Estimation.
- Authors
ZHANG Zhao; HU Yuhui; ZHOU Dong; WU Ligang; YAO Weiran; LI Peng
- 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.
- Subjects
SPACE debris; DEEP learning; ARTIFICIAL neural networks; INFORMATION retrieval; REGRESSION analysis
- Publication
Transactions of Nanjing University of Aeronautics & Astronautics, 2024, Vol 41, Issue 1, p122
- ISSN
1005-1120
- Publication type
Article
- DOI
10.16356/j.1005‑1120.2024.01.010