适应着舰引导大距离跨度的高精度单目视觉位姿测量

doi:10.7527/S1000-6893.2025.31568

Abstract

Abstract:

The autonomous landing guidance involves a large distance span， resulting in significant scale variations of the ship target in the image sequences obtained through monocular vision guidance. Existing pose measurement methods struggle to achieve high-precision monocular vision pose measurement across such a wide distance range. For current monocular vision pose measurement methods based on sparse keypoint sets， this paper focuses on improving the accuracy of keypoint detection， and analyzes the impact of target size and network input size on keypoint detection accuracy. Furthermore， this paper proposes a novel monocular vision pose measurement method based on multiple components， balancing both accuracy and efficiency. By using sparse keypoint sets to represent components in a simplified manner， and building on a coarse pose estimation of the overall ship target components， this method introduces a path aggregation feature pyramid network and a hierarchical encoding module to achieve high-precision detection of local component keypoints. Subsequently， by integrating the high-precision keypoint detection results of all components and solving the Perspective-n-Points （PnP） problem， the method achieves robust and high-precision pose measurement across the large distance span required for landing guidance. Simulation experiments and scaled physical experiments demonstrate that the proposed method achieves robust and high-precision monocular pose measurement across the large distance span for landing guidance， outperforming existing methods， with an average single-frame inference time of approximately 40 ms on embedded platforms.

Key words: monocular, landing guidance, pose measurement, deep learning, keypoint detection

CLC Number:

V249.32

Lin CHEN, Xiwen GU, Zhiying CHEN, Zhuo ZHANG, Xiaoliang SUN. High-precision monocular vision pose measurement for large distance span in carrier landing guidance[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(15): 331568.

Figures/Tables 23

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Table 5

Scaled physical experiment pose estimation error

方法	$e (x) / 像素$	$e (R) / (°)$	$e (t) / %$
PP-TinyPose	1.21	0.43	0.71
ViTPose	0.59	0.29	0.40
RTMPose	0.72	0.30	0.47
本文方法	0.44	0.27	0.34

Table 5

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References 27

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方法	输入尺寸	帧率/（帧·s^-1）	浮点运算数/G
RTMPose	256×256	95.39	5.508
	384×384	83.25	12.385
	512×512	70.05	22.012

参数	俯仰/（°）	偏航/（°）	滚转/（°）	X/m	Y/m	Z/m
周期/s	6	5	3	5	4	4
初始值	11	0	0	0	-2 600	218.7
扰动幅值	1	3	5	0~5	0~3	0~0.5

方法	平均误差/像素
方法	>2.0 km	1.0~2.0 km	0.5~1.0 km	0.2~0.5 km	<0.2 km
PP-TinyPose	0.55	0.80	1.56	2.83	4.69
ViTPose	0.12	0.19	0.49	0.61	1.30
RTMPose	0.16	0.24	0.43	0.55	1.21
本文方法	0.07	0.12	0.16	0.29	0.54

方法	平均误差/像素
方法	>2.0 km	1.0~2.0 km	0.5~1.0 km	0.2~0.5 km	<0.2 km
粗略阶段结果	0.16	0.25	0.42	0.55	1.10
RTMPose	0.10	0.14	0.28	0.35	0.59
+PAFPN	0.07	0.12	0.18	0.33	0.57
+HEM	0.08	0.13	0.17	0.30	0.54
+PAFPN+HEM	0.07	0.10	0.16	0.29	0.54

参数	数值
CPU	8核Carmel ARMv8.2 64处理器
GPU	512核Volta架构
内存	32 GB
储存	32 GB
峰值算力	32 TOPS

High-precision monocular vision pose measurement for large distance span in carrier landing guidance

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方法	目标检测耗时/ms	关键点检测耗时/ms	位姿估计耗时/ms	总耗时/ms
RTMPose	9.2	20.8	1.8	31.8
本文方法	9.2	28.9	1.9	40.0