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Sensors 2018, 18(4), 933; https://doi.org/10.3390/s18040933

Robust Spacecraft Component Detection in Point Clouds

1
Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China
2
Beijing Key Laboratory of Digital Media, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Received: 10 January 2018 / Revised: 10 March 2018 / Accepted: 13 March 2018 / Published: 21 March 2018
(This article belongs to the Special Issue Sensor Signal and Information Processing)
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Abstract

Automatic component detection of spacecraft can assist in on-orbit operation and space situational awareness. Spacecraft are generally composed of solar panels and cuboidal or cylindrical modules. These components can be simply represented by geometric primitives like plane, cuboid and cylinder. Based on this prior, we propose a robust automatic detection scheme to automatically detect such basic components of spacecraft in three-dimensional (3D) point clouds. In the proposed scheme, cylinders are first detected in the iteration of the energy-based geometric model fitting and cylinder parameter estimation. Then, planes are detected by Hough transform and further described as bounded patches with their minimum bounding rectangles. Finally, the cuboids are detected with pair-wise geometry relations from the detected patches. After successive detection of cylinders, planar patches and cuboids, a mid-level geometry representation of the spacecraft can be delivered. We tested the proposed component detection scheme on spacecraft 3D point clouds synthesized by computer-aided design (CAD) models and those recovered by image-based reconstruction, respectively. Experimental results illustrate that the proposed scheme can detect the basic geometric components effectively and has fine robustness against noise and point distribution density. View Full-Text
Keywords: geometric primitive; component detection; spacecraft; 3D point clouds geometric primitive; component detection; spacecraft; 3D point clouds
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Wei, Q.; Jiang, Z.; Zhang, H. Robust Spacecraft Component Detection in Point Clouds. Sensors 2018, 18, 933.

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