2017
DOI: 10.1155/2017/4535316
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Pose and Shape Reconstruction of a Noncooperative Spacecraft Using Camera and Range Measurements

Abstract: Recent interest in on-orbit proximity operations has pushed towards the development of autonomous GNC strategies. In this sense, optical navigation enables a wide variety of possibilities as it can provide information not only about the kinematic state but also about the shape of the observed object. Various mission architectures have been either tested in space or studied on Earth. The present study deals with on-orbit relative pose and shape estimation with the use of a monocular camera and a distance sensor… Show more

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Cited by 24 publications
(12 citation statements)
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“…As the target tumbling in orbit, new rectangular features are observed and must be added to the state vector, and some features disappear and can be only propagated in the filter. This strategy has been proven effective in literatures [6,7,19].…”
Section: Is the Attitude Rotation Matrix From The Frame O T X T Y T Zmentioning
confidence: 99%
See 1 more Smart Citation
“…As the target tumbling in orbit, new rectangular features are observed and must be added to the state vector, and some features disappear and can be only propagated in the filter. This strategy has been proven effective in literatures [6,7,19].…”
Section: Is the Attitude Rotation Matrix From The Frame O T X T Y T Zmentioning
confidence: 99%
“…Complex image processing techniques are developed to measure the relative position and relative attitude of noncooperative targets [4,5]. Volpe et al proposed an on-orbit relative pose and shape estimation with the use of a monocular camera and a distance sensor [6,7]. In China, Caixiu proposed a method by using a single camera to measure the relative pose by utilizing a solar panel triangle structure and a circular docking ring [8].…”
Section: Introductionmentioning
confidence: 99%
“…Vision system also has many schemes such as monocular vision [8], stereo vision [9], and active vision with structure light [10]. Besides, the active cameras such as Flash-LIDAR's can be used to detect the unknown object [11].…”
Section: Introductionmentioning
confidence: 99%
“…In the paper [24], a purely monocular-based vision SLAM algorithm for pose tracking and shape reconstruction of an unknown target is presented; however, it is assumed that the target rotates with constant angular velocity and the target inertia tensor is not estimated. The fusion of the data obtained from the monocular camera and a range sensor is utilized in the algorithm for the pose, and shape reconstruction of a noncooperative spacecraft is considered in the paper [25]; the unscented Kalman filter is used for the estimation of 6n + 17 variables, where n is the number of the feature points. The current paper is quite similar to [25], but we divided the pose determination algorithm into two stages-the most computational power consumed determination of the reference point position and inertia tensor using least square method and the second, determination of the relative pose only based on the data obtained by the first stage.…”
Section: Introductionmentioning
confidence: 99%
“…The fusion of the data obtained from the monocular camera and a range sensor is utilized in the algorithm for the pose, and shape reconstruction of a noncooperative spacecraft is considered in the paper [25]; the unscented Kalman filter is used for the estimation of 6n + 17 variables, where n is the number of the feature points. The current paper is quite similar to [25], but we divided the pose determination algorithm into two stages-the most computational power consumed determination of the reference point position and inertia tensor using least square method and the second, determination of the relative pose only based on the data obtained by the first stage. So, it is not necessary to estimate the reference points and the inertia tensor all the time since it is assumed that they are constant since the unknown satellite is considered as a rigid body.…”
Section: Introductionmentioning
confidence: 99%