LIDAR-based Relative Navigation of Non-Cooperative Objects Using Point Cloud Descriptors

Rhodes, Andrew; Kim, Eric; Christian, John A.; Evans, Thomas

doi:10.2514/6.2016-5517

Cited by 23 publications

(23 citation statements)

References 14 publications

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“…Other solutions involve template matching for pose initialization and then exploit the typical ICP [25] for frame-to-frame pose estimation [9,16,17,26,27]. Variants of that methodology substitute the template matching scheme for pose initialization either with Principle Component Analysis (PCA) [9,28] or with global 3D feature matching using the Oriented Unique Repeatable Clustered Viewpoint Feature Histogram (OUR-CVFH) [14,29]. Other solutions available in the literature fuse pose estimation based on OUR-CVFH or on Spin Images [30] (a 3D local feature descriptor) and ICP, with gyroscopic data and then perform Target platform tracking using a Multiplicative Extended Kalman Filter (MEKF) [10,28,31].…”

Section: Introductionmentioning

confidence: 99%

High-speed multi-dimensional relative navigation for uncooperative space objects

2019

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This work proposes a high-speed Light Detection and Ranging (LIDAR) based navigation architecture that is appropriate for uncooperative relative space navigation applications. In contrast to current solutions that exploit 3D LIDAR data, our architecture transforms the odometry problem from the 3D space into multiple 2.5D ones and completes the odometry problem by utilizing a recursive filtering scheme. Trials evaluate several current state-of-theart 2D keypoint detection and local feature description methods as well as recursive filtering techniques on a number of simulated but credible scenarios that involve a satellite model developed by Thales Alenia Space (France). Most appealing performance is attained by the 2D keypoint detector Good Features to Track (GFFT) combined with the feature descriptor KAZE, that are further combined with either the H∞ or the Kalman recursive filter. Experimental results demonstrate that compared to current algorithms, the GFTT/ KAZE combination is highly appealing affording one order of magnitude more accurate odometry and a very low processing burden, which depending on the competitor method, may exceed one order of magnitude faster computation.

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Section: Introductionmentioning

confidence: 99%

High-speed multi-dimensional relative navigation for uncooperative space objects

2019

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“…Current space navigation solutions that rely on computer vision concepts involve regional 3D feature description and not local description (A. Rhodes, Kim, Christian, & Evans, 2016; A. P. Rhodes et al, 2017), neglecting the state-of-the-art performance afforded by the latter type. It should be noted that (A.…”

Section: Introductionmentioning

confidence: 99%

“…It should be noted that (A. Rhodes et al, 2016) made an attempt using the local feature descriptor Spin Images (Andrew Edie Johnson & Hebert, 1998), however the research concluded that this descriptor is not optimal for space navigation. To the best our knowledge none other local descriptor has been used to date.…”

Section: Introductionmentioning

confidence: 99%

Evaluating 3D local descriptors and recursive filtering schemes for LIDAR‐based uncooperative relative space navigation

Kechagias‐Stamatis

Aouf

Dubanchet³

2019

Journal of Field Robotics

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We propose a Light Detection and Ranging (LIDAR) based relative navigation scheme that is appropriate for uncooperative relative space navigation applications. Our technique combines the encoding power of the 3D local descriptors that are matched exploiting a correspondence grouping scheme, with the robust rigid transformation estimation capability of the proposed adaptive recursive filtering techniques. Trials evaluate several current state-of-the-art 3D local descriptors and recursive filtering techniques on a number of both real and simulated scenarios that involve various space objects including satellites and asteroids. Results demonstrate that the proposed architecture affords a 50% odometry accuracy improvement over current solutions, while also affording a low computational burden. From our trials we conclude that HoD-S combined with the adaptive αβ filtering poses the most appealing combination for the majority of the scenarios evaluated, as it combines high quality odometry with a low processing burden.

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“…Many model-based approaches have been recently proposed in the literature. Most of them are designed to operate on raw data, i.e., point-based algorithms [ 14 , 15 , 16 , 17 , 18 ], while others rely on the extraction of natural features or more complex descriptors, i.e., feature-based algorithms [ 19 , 20 ]. The results presented in these works highlight that some open challenges still need to be addressed.…”

Section: Introductionmentioning

confidence: 99%

Hardware in the Loop Performance Assessment of LIDAR-Based Spacecraft Pose Determination

Opromolla

Fasano

Rufino

et al. 2017

Sensors

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In this paper an original, easy to reproduce, semi-analytic calibration approach is developed for hardware-in-the-loop performance assessment of pose determination algorithms processing point cloud data, collected by imaging a non-cooperative target with LIDARs. The laboratory setup includes a scanning LIDAR, a monocular camera, a scaled-replica of a satellite-like target, and a set of calibration tools. The point clouds are processed by uncooperative model-based algorithms to estimate the target relative position and attitude with respect to the LIDAR. Target images, acquired by a monocular camera operated simultaneously with the LIDAR, are processed applying standard solutions to the Perspective-n-Points problem to get high-accuracy pose estimates which can be used as a benchmark to evaluate the accuracy attained by the LIDAR-based techniques. To this aim, a precise knowledge of the extrinsic relative calibration between the camera and the LIDAR is essential, and it is obtained by implementing an original calibration approach which does not need ad-hoc homologous targets (e.g., retro-reflectors) easily recognizable by the two sensors. The pose determination techniques investigated by this work are of interest to space applications involving close-proximity maneuvers between non-cooperative platforms, e.g., on-orbit servicing and active debris removal.

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LIDAR-based Relative Navigation of Non-Cooperative Objects Using Point Cloud Descriptors

Cited by 23 publications

References 14 publications

High-speed multi-dimensional relative navigation for uncooperative space objects

High-speed multi-dimensional relative navigation for uncooperative space objects

Evaluating 3D local descriptors and recursive filtering schemes for LIDAR‐based uncooperative relative space navigation

Hardware in the Loop Performance Assessment of LIDAR-Based Spacecraft Pose Determination

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