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Robust Spacecraft Component Detection in Point Clouds.

Quanmao Wei1,2, Zhiguo Jiang3,4, Haopeng Zhang5,6

  • 1Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China. weiqm@buaa.edu.cn.

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Summary
This summary is machine-generated.

This study presents a new method for automatically identifying spacecraft components like cylinders, planes, and cuboids in 3D point clouds. The approach enhances space situational awareness and on-orbit operations.

Keywords:
3D point cloudscomponent detectiongeometric primitivespacecraft

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Area of Science:

  • Aerospace Engineering
  • Computer Vision
  • Robotics

Background:

  • Spacecraft component identification is crucial for on-orbit operations and space situational awareness.
  • Spacecraft are typically composed of geometric primitives such as planes, cuboids, and cylinders.

Purpose of the Study:

  • To develop a robust automatic detection scheme for basic spacecraft components in 3D point clouds.
  • To enable mid-level geometry representation of spacecraft.

Main Methods:

  • Iterative detection of cylinders using energy-based geometric model fitting.
  • Hough transform for plane detection, followed by bounding and minimum bounding rectangle description.
  • Cuboid detection based on pairwise geometric relations between detected planar patches.

Main Results:

  • Successfully detected basic geometric components (cylinders, planes, cuboids) in 3D spacecraft point clouds.
  • Demonstrated robustness against noise and varying point distribution densities.
  • Generated a mid-level geometric representation of spacecraft.

Conclusions:

  • The proposed scheme effectively detects fundamental spacecraft components from 3D point cloud data.
  • The method offers a reliable approach for spacecraft component recognition, aiding in space situational awareness and operational tasks.