Research on Unknown Space Target Pose Estimation Method Based on Point Cloud

As space in-orbit missions continue to develop, many uncooperative unknown space targets have occupied valuable orbital resources, seriously threatening the safety of spacecraft in orbit operation. One of the key technologies for in orbit space services is the determination of the relative attitude...

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Vydáno v:IEEE access Ročník 12; s. 149381 - 149390
Hlavní autoři: Zhang, Huan, Zhang, Yang, Feng, Qingjuan, Zhang, Kebei
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 2024
Témata:
Data models
dataset
Feature extraction
Image sensors
Point cloud compression
point cloud registration
Pose estimation
Sensor phenomena and characterization
Sensors
Solid modeling
Space target
Space vehicles
Three-dimensional displays
ISSN:2169-3536, 2169-3536
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Popis
Shrnutí:As space in-orbit missions continue to develop, many uncooperative unknown space targets have occupied valuable orbital resources, seriously threatening the safety of spacecraft in orbit operation. One of the key technologies for in orbit space services is the determination of the relative attitude between the target and the tracker. The existing research mainly focuses on accurately determining the position and attitude of large satellites with known features such as solar panels, and lacks research verification for small space targets with complex shapes. This article first proposes a method for synthesizing visual perception data of space targets, synthesizing a batch of space targets perception data containing space fragments. In order to achieve accurate pose estimation of space targets with various shape features, a two-stage pose estimation method combining random sampling consistency algorithm and improved ICP algorithm is proposed. Finally, this article conducted extensive experimental validation on the proposed synthetic point cloud dataset. Compared with other point cloud registration algorithms, the proposed method achieves better registration results on synthesized multi scene simulation data.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3477296