基于单目视觉与测距信息的无人机集群定位方法

doi:10.7527/S1000-6893.2024.31281

Abstract

Abstract:

Unmanned Aerial Vehicle （UAV） swarms play a pivotal role in the low-altitude economy. Accurate swarm positioning information underpins mission coordination， resource optimization， and efficient scheduling among drones， thereby facilitating the sustainable advancement of the low-altitude economy. In complex environments， however， Global Satellite Navigation System （GNSS） signals may be disrupted， rendering it difficult for UAV swarms to obtain accurate positioning data and compromising their ability to function collaboratively. To address this challenge in GNSS-denied environments， this paper presents a UAV swarm positioning method that integrates monocular vision and ranging information. Visual Odometry （VO） is employed to enable autonomous positioning for each UAV within the swarm. A communication framework is designed to transmit only essential data， including visual keyframes， pose frames， and map points， to the central server， thus reducing communication bandwidth. The concept of pose frame is introduced to address the limitation that keyframes cannot fuse with ranging information. The central server aligns maps from different UAVs based on the common view relationships between keyframes or the constraints between pose frames and their corresponding ranging data. The server then fuses and optimizes these maps using both visual and ranging information， achieving accurate swarm positioning. After global optimization， the server sends the corrected keyframe and map point data back to the local map of UAV’s VO to further enhance positioning accuracy. The proposed method is validated through simulations and experiments. Results demonstrate that the swarm positioning error is reduced to 0.49 m， outperforming current state-of-the-art visual positioning methods. Additionally， the scale error is reduced to 3.2%， effectively resolving the problem of scale ambiguity inherent in monocular visual positioning. This method proposed enables precise UAV swarm positioning based solely on inter-UAV ranging information， eliminating the need for shared visual features， and providing robust positioning data for UAV swarms operating in complex environments.

Key words: UAV swarm, visual positioning, range measurement, swarm positioning, graph optimization

CLC Number:

V249.32

Kun LI, Shuhui BU, Jiapeng LI, Juboxi WANG, Pengcheng HAN, Xiaohan LI, Haowei LI. UAV swarm positioning method based on monocular vision and ranging information[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(11): 531281.

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图像序列	飞行时间/s	轨迹长度/m	飞行速度/（m·s^-1）	光照变换
MH_01	182	80.6	0.44	光亮
MH_02	150	73.5	0.49	光亮
MH_03	132	130.9	0.99	光亮
MH_04	99	91.7	0.93	黑暗
MH_05	111	58.6	0.88	黑暗

算法	均方根绝对轨迹误差E_{RMS AT}/m
算法	MH_01	MH_02	MH_03	MH_04	MH_05
SVO	0.100	0.120	0.410	0.430	0.300
DSO	0.046	0.046	0.172	3.810	0.110
ORB-SLAM2	0.071	0.067	0.071	0.082	0.060
ORB-SLAM3	0.018	0.270	0.028	0.180	0.072
VO节点	0.103	0.940	0.168	0.087	0.076
本文方法	0.073	0.063	0.074	0.081	0.061

算法	定位精度/m
算法	MH_01	MH_02	MH_03	MH_04	MH_05
CCM-SLAM	0.061	0.063	0.048	0.121
单无人机	0.073	0.063	0.074	0.079	0.061
本文方法	0.058	0.061	0.052	0.079	0.051

算法	定位尺度精度/%
算法	MH_01	MH_02	MH_03	MH_04	MH_05
本文方法	1.2	1.7	2.3	1.4	4.2

定位方法	定位误差/m	尺度误差/%
本文方法	0.25	3.7
CCM-SLAM	0.28

UAV swarm positioning method based on monocular vision and ranging information

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References 31

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定位轨迹	定位精度/m
定位轨迹	CMM-SLAM	本文方法
无人机1	0.620	0.410
无人机2	0.440	0.460
无人机3		0.670
集群	0.530	0.490

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