Monocular-Vision-Based Relative Pose Estimation of Noncooperative Spacecraft Using Multicircular Features

Long, Chenrong; Hu, Qinglei

doi:10.1109/tmech.2022.3181681

Cited by 9 publications

(1 citation statement)

References 28 publications

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“…The feature-point-based methods predefine some remarkable 3D feature points on the target 3D model a priori and then match the 3D feature points against the corresponding image points. The pose is solved from the 2D-3D matching by the Perspective-n-Point (PnP) algorithm (Huo et al, 2020;Conway et al;Hu et al, 2021;Black et al, 2021;Leroy et al, 2001;Capuano et al, 2020;Chen et al, 2019;Huang et al, 2021b;Stacey and D'Amico, 2018;Liu and Hu, 2014;Peng et al, 2020;Crivellaro et al, 2017;Meng et al, 2018;Rowell et al, 2015;Long and Hu, 2022;Liu et al, 2020a;Christian, 2015;Li and Xu, 2018;Cassinis et al, 2022). However, it is difficult to specify sufficient remarkable 3D feature points on an asteroid since it is irregular and textureless (Huang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Model-based monocular 6-degree-of-freedom pose tracking for asteroid

Tang,

Liu,

et al. 2024

Front. Space Technol.

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In this paper, we present a novel vision-based framework to track the 6-DoF pose of an asteroid in real time with the 3D contour of the asteroid as a feature. During pose tracking, at the beginning time of tracking, the tracking system is initialized by a pose retrieval method. At each subsequent time instant, given the 3D mesh model of an asteroid, with the initial pose and its covariance given by the square root cubature Kalman Filter (SCKF), the 3D mesh segments constituting the 3D asteroid contour are efficiently extracted from the 3D mesh model. Then, in the input asteroid image, we search the image points corresponding to the extracted 3D segments within the searching range defined by the initial pose and its covariance. After that, the asteroid pose is determined in real time by minimizing the angles between the back-projection lines of the searched image points and the projection planes of the corresponding 3D segments, which is much more robust to the position change of the asteroid and asteroid size. The covariance matrix of the pose is inferred from the Cartesian noise model in the first order. Eventually, the SCKF is derived from the second-order auto regression to generate the final pose estimate and give the initial pose and its covariance for the next time instant. The synthetic trials quantitatively validate the real-time performance, robustness, and accuracy of our algorithm in dark space, different imaging distances, lighting conditions, image noise, model error, and initial pose error, and meanwhile, the real trial qualitatively shows the effectiveness of our method.

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