Target Localization from 3D data for On-Orbit Autonomous Rendezvous &amp;#x00026; Docking

Ruel, Stephane; Luu, Tim; Anctil, M.; Gagnon, Stéphane

doi:10.1109/aero.2008.4526516

Cited by 26 publications

(18 citation statements)

References 10 publications

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“…about 500 points in average, much less than typical applications based on detecting some thousands of points [32]. Regarding the noise parameters, the selected data (σ LOS ¼0.00071, σ RANGE ¼25 mm, and P O ¼ 5-7%) are relevant to a worst-case scenario since spaceborne LIDARs have typically better noise characteristics [28].…”

Section: Resultsmentioning

confidence: 99%

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Uncooperative pose estimation with a LIDAR-based system

et al. 2015

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Section: Resultsmentioning

confidence: 99%

“…Furthermore, a percentage of outlier (P O ) has been considered for each target component. The outliers are selected in the detected point cloud thanks to a random extraction from a uniform distribution and are characterized by a range uncertainty that is four times σ RANGE [28].…”

Section: Measurement Modelmentioning

confidence: 99%

Uncooperative pose estimation with a LIDAR-based system

et al. 2015

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“…Innovative computer vision algorithms developed in-house at Neptec allow this process to happen in real-time on TriDAR's embedded flight computer while achieving the necessary robustness and reliability expected for mission critical operations [4] [5]. Initialization of the tracking process is performed by automatic acquisition algorithms also developed in house at Neptec [8]. Fast data acquisition has been achieved by implementing a smart scanning strategy referred to as More Information Less Data (MILD) where only the necessary data to perform the pose estimation is acquired by the sensor.…”

Section: Tridar Relative Navigation Vision Systemmentioning

confidence: 99%

STS-128 on-orbit demonstration of the TriDAR targetless rendezvous and docking sensor

Ruel

Luu

2010

2010 IEEE Aerospace Conference

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Neptec has developed a vision system for autonomous rendezvous and docking in space that does not require the use of cooperative markers, such as retroreflectors, on the target spacecraft. 12 The system uses an active TriDAR 3D sensor along with embedded model based tracking algorithms to provide, out of the box, 6 degree of freedom (6DOF) relative pose information in realtime. The TriDAR (triangulation + LIDAR) sensing technology combines active triangulation and Time-ofFlight (TOF) ranging techniques within a single optical path. This design takes advantage of the complementary nature of these two technologies to provide optimal 3 dimensional data from several kilometers all the way to docking. A thermal imager is also included to provide bearing information at long range.

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“…In the case of the monocular approaches, a possible solution is to build the database of images with a hierarchical structure in which similar views are clustered at the lower levels of the hierarchy [ 9 , 10 ]. A similar approach, in the case of 3D techniques, is adopted by the polygonal aspect hashing algorithm, which limits the search space to the set of poses that allow the reference model to have at least partial overlapping surfaces with the input data [ 11 ]. A different approach, specifically designed for elongated targets, is to reduce the size of the database by splitting the estimation of the pose parameters in two phases: firstly, the two angles which identify the target main axis are determined by exploiting the principal component analysis, and, secondly, a 3D binary TM algorithm is applied to a 4-DOF database to compute the remaining rotation parameter and the relative position vector [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

A Model-Based 3D Template Matching Technique for Pose Acquisition of an Uncooperative Space Object

Opromolla

Fasano

Rufino

et al. 2015

Sensors

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This paper presents a customized three-dimensional template matching technique for autonomous pose determination of uncooperative targets. This topic is relevant to advanced space applications, like active debris removal and on-orbit servicing. The proposed technique is model-based and produces estimates of the target pose without any prior pose information, by processing three-dimensional point clouds provided by a LIDAR. These estimates are then used to initialize a pose tracking algorithm. Peculiar features of the proposed approach are the use of a reduced number of templates and the idea of building the database of templates on-line, thus significantly reducing the amount of on-board stored data with respect to traditional techniques. An algorithm variant is also introduced aimed at further accelerating the pose acquisition time and reducing the computational cost. Technique performance is investigated within a realistic numerical simulation environment comprising a target model, LIDAR operation and various target-chaser relative dynamics scenarios, relevant to close-proximity flight operations. Specifically, the capability of the proposed techniques to provide a pose solution suitable to initialize the tracking algorithm is demonstrated, as well as their robustness against highly variable pose conditions determined by the relative dynamics. Finally, a criterion for autonomous failure detection of the presented techniques is presented.

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Target Localization from 3D data for On-Orbit Autonomous Rendezvous & Docking

Cited by 26 publications

References 10 publications

Uncooperative pose estimation with a LIDAR-based system

Uncooperative pose estimation with a LIDAR-based system

STS-128 on-orbit demonstration of the TriDAR targetless rendezvous and docking sensor

A Model-Based 3D Template Matching Technique for Pose Acquisition of an Uncooperative Space Object

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