Transactions of Nanjing University of Aeronautics & Astronautics
DMANet: Dense Multi-scale Attention Network for Space Non-cooperative Object Pose Estimation
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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.
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Clc Number:
TP391
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