ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Non-cooperative target pose estimation from monocular images based on lightweight neural network
Received date: 2024-01-29
Revised date: 2024-04-07
Accepted date: 2024-04-26
Online published: 2024-04-30
Supported by
National Natural Science Foundation of China(12302252);Research Program of National University of Defense Technology(ZK24-31)
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
Zi WANG , Jinghao WANG , Yang LI , Zhang LI , Qifeng YU . Non-cooperative target pose estimation from monocular images based on lightweight neural network[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2024 , 45(22) : 330248 -330248 . DOI: 10.7527/S1000-6893.2024.30248
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