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Acta Aeronautica et Astronautica Sinica ›› 2024, Vol. 45 ›› Issue (22): 330248.doi: 10.7527/S1000-6893.2024.30248

• Electronics and Electrical Engineering and Control • Previous Articles    

Non-cooperative target pose estimation from monocular images based on lightweight neural network

Zi WANG1,2, Jinghao WANG1,2, Yang LI1,2, Zhang LI1,2(), Qifeng YU1,2   

  1. 1.College of Aerospace Science and Engineering,National University of Defense Technology,Changsha 410073,China
    2.Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation,Changsha 410073,China
  • Received:2024-01-29 Revised:2024-04-07 Accepted:2024-04-26 Online:2024-05-23 Published:2024-04-30
  • Contact: Zhang LI E-mail:zhangli_nudt@163.com
  • Supported by:
    National Natural Science Foundation of China(12302252);Research Program of National University of Defense Technology(ZK24-31)

Abstract:

Estimating the pose of non-cooperative targets from monocular images stands as a pivotal technology for future space missions. Recent advancements in deep neural networks have surpassed traditional methods in pose measurement accuracy. However, these networks often entail a high number of parameters and significant computational complexity. This poses a challenge for deployment in on-orbit applications where real-time measurement is crucial, as computational resources are limited. Reducing the number of network parameters compromises the ability to extract representative features, leading to degraded pose estimation performance. To tackle this problem, we present an approach using lightweight neural networks that maintains high accuracy in pose estimation, which is a task far from trivial. Our solution involves a novel semantic keypoint localization method. We develop a lightweighted neural network model with a mere 1.1 ×106 parameters. To enhance the precision of semantic keypoint localization and subsequent pose estimation, we introduce a heatmap decoding technique that allows for sub-pixel level accuracy, while enabling end-to-end supervision of semantic keypoint localization. Moreover, we develop an auxiliary layer supervised training method to further refine the accuracy of semantic keypoint localization. Experiments on public datasets demonstrate that our method not only achieves the highest pose measurement accuracy among all lightweight models with fewer than 107 parameters, but also sets a new benchmark. Additionally, tests on embedded development boards reveal that our method attains measurement frequencies of 5 Hz and 11 Hz in 10 W and 30 W power modes, respectively.

Key words: pose measurement, monocular image, lightweight, deep learning, semantic keypoint

CLC Number: