Intelligent LIDAR scanning region selection for satellite pose estimation

Shahid, Kamran; Okouneva, Galina

doi:10.1016/j.cviu.2006.11.005

Cited by 15 publications

(10 citation statements)

References 5 publications

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“…Interestingly, the average value of J for coarse Let x, y, z, α, β, γ be the errors between the estimated pose and the ground truth. Then, two accuracy measures [27] have been considered…”

Section: Registration Resultsmentioning

confidence: 99%

3D registration of laser range scenes by coincidence of coarse binary cubes

Martínez

Reina

Mandow

et al. 2011

Machine Vision and Applications

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Scene registration of a pair of three-dimensional (3D) range images is a 6D optimization problem usually required in mobile robotics. This problem is different from object registration, since all scan directions and depths may contain relevant data, and because farther regions are sampled with lower densities. The paper proposes an efficient scene matching method based on the concept of coarse binary cubes. An integer objective function is defined as the number of coincident cubes between both scans. This is a metric of the degree of overlap that does not employ point distances. Its value is obtained without actually using any 3D grid data structure, with a computational complexity of order O(n), where n represents the number of laser points. This objective function is optimized with a globalized version of the downhill Simplex algorithm to avoid local minima. Experimental results are presented from indoor and outdoor environments with different degrees of structuring. The effect of cube size and the number of vertices on registration performance has been analyzed. Besides, experiments show that the proposed method achieves similar accuracy as iterative closest points (ICP) and normal distribution transform (NDT), while it improves both computation time and robustness against initial misalignments.

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Section: Registration Resultsmentioning

confidence: 99%

3D registration of laser range scenes by coincidence of coarse binary cubes

Martínez

Reina

Mandow

et al. 2011

Machine Vision and Applications

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“…While Simon used a sparse set of key points for bone alignment, Shahid & Okouneva (2007) used the same form of discrete-point constraint analysis and applied it to point clouds generated by windowed areas of spacecraft objects. By localizing geometric features, the optimal local scan areas for pose estimation can be identified.…”

Section: Pca Indices For Pose Estimationmentioning

confidence: 99%

“…The descriptions of the ICP program above will be expanded upon further below. Portions of this work are based on the framework outlined by Shahid (2007).…”

Section: Implementation Of Icp and Pca Indices In A Simulated Environmentmentioning

confidence: 99%

Principal Component Analysis for ICP Pose Estimation of Space Structures

Mark¹

2023

Preprint

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This thesis investigates how geometry of complex objects is related to LIDAR scanning with the Iterative Closest Point (ICP) pose estimation and provides statistical means to assess the pose accuracy. LIDAR scanners have become essential parts of space vision systems for autonomous docking and rendezvous. Principal Componenet Analysis based geometric constraint indices have been found to be strongly related to the pose error norm and the error of each individual degree of freedom. This leads to the development of several strategies for identifying the best view of an object and the optimal combination of localized scanned areas of the object's surface to achieve accurate pose estimation. Also investigated is the possible relation between the ICP pose estimation accuracy and the districution or allocation of the point cloud. The simulation results were validated using point clouds generated by scanning models of Quicksat and a cuboctahedron using Neptec's TriDAR scanner.

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“…Autonomous rendezvous and docking between satellites offers a cost-effective alternative to manned missions to repair and refuel satellites on orbit, and extends the range of such missions to regions of space such as GEO that were not previously feasible. 3 The central problem is estimation of the relative position and attitude (pose) between the two satellites. Many combinations of instruments could be mechanized to approach this problem.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Relative Attitude and Trajectory Estimation for Autonomous Rendezvous Using Flash LIDAR

McMahon

Gehly

Axelrad

2014

AIAA/AAS Astrodynamics Specialist Conference

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Initial results of a study on the use of flash LIDAR for relative pose and trajectory estimation are presented. Analysis is conducted for distant approach phases where flash LIDAR measurements are processed with no information about the target body shape and attitude, and for close approach phases where the relative pose is estimated simultaneously with the relative trajectory. In both cases, very good relative navigation accuracy can be obtained with errors based on current flash LIDAR models. The distant navigation analysis considered the necessary frequency of flash LIDAR data needed for accurate solutions, the effect of ignoring the target shape, and the growth of the achieved errors in the case of sensor malfunction. The close approach results are focused on the implementation of a combination of the Optimal Linear Attitude and Translation Estimator (OLTAE) algorithm for processing feature based tracking into relative attitude and translation measurements, combined with an extended Kalman filter (EKF) for estimating the full 6-DOF relative dynamic state.

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Intelligent LIDAR scanning region selection for satellite pose estimation

Cited by 15 publications

References 5 publications

3D registration of laser range scenes by coincidence of coarse binary cubes

3D registration of laser range scenes by coincidence of coarse binary cubes

Principal Component Analysis for ICP Pose Estimation of Space Structures

Enhancing Relative Attitude and Trajectory Estimation for Autonomous Rendezvous Using Flash LIDAR

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