Simulation Results of ISS AR&amp;D Using Only Range Images

Rohrschneider, Reuben R.; Tandy, William D.; Bladt, Jeff; Gravseth, Ian

doi:10.2514/6.2012-4781

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…The non-linearity was not corrected on the STORRM mission because it did not affect the range measurements to the retroreflectors on the docking target. Computation of relative rotations has been very successful when using noisy simulated point clouds, indicating that improved tuning and calibration of the flash LIDAR unit would enable computation of relative rotations with sufficient accuracy for AR&D. Figure 7 shows the error in the computed relative rotations for the final portion of docking [12]. The red curve is the rotation about the viewing vector, and is not computed, and so shows zero error.…”

Section: Figure 6 Comparison Of Relative Position Trajectories From mentioning

confidence: 95%

An Overview of Ball Flash LIDAR and Related Technology Development

Rohrschneider

Masciarelli

Miller

et al. 2013

AIAA Guidance, Navigation, and Control (GNC) Conference

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Ball Aerospace has been developing Flash LIDAR systems for more than 7 years, and space qualified their first system on the Sensor Test for Orion Relative-navigation Risk Mitigation (STORRM) mission in May of 2011 on STS-134. The STORRM unit demonstrated the capabilities of the flash LIDAR system for relative navigation, but other applications exist, including science applications, and landing risk mitigation on Earth and other planets. This paper provides an overview of flash LIDAR sensor systems, describes the applications and scenarios where they provide advantages over scanning systems, and provides an overview of related technology development efforts at Ball Aerospace. The current development efforts include relative navigation applications, 6-DOF relative pose determination, landing hazard detection, electronic laser beam steering, and sensor upgrades that include electronic architecture modifications.

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Section: Figure 6 Comparison Of Relative Position Trajectories From mentioning

confidence: 95%

An Overview of Ball Flash LIDAR and Related Technology Development

Rohrschneider

Masciarelli

Miller

et al. 2013

AIAA Guidance, Navigation, and Control (GNC) Conference

Self Cite

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“…Recent literature contains a variety of approaches for performing spacecraft RelNav using 3D point-clouds [22,25,26,27,19,28,29,30,16]. Most methods use the Iterative Closest Point (ICP) algorithm [31] or variant thereof [32,33,34,35] to align the sensed point-cloud with a known model.…”

Section: Uncorrelated Pose Estimates In Navigation Filtersmentioning

confidence: 99%

“…Consequently, the method and accuracy of ICP initialization for both initial acquisition and frame-to-frame tracking is of importance. Literature contains a variety of proposals for ICP initialization using methods such as polygonal aspect hashing [38], principal component analysis [39], template matching [28,29], and feature histograms [25,19,30,40,16].…”

Section: Uncorrelated Pose Estimates In Navigation Filtersmentioning

confidence: 99%

Utilization of Scale in Keypoint Detection and Feature Description

Rhodes¹

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Ubiquitous availability of inexpensive three dimensional (3D) sensors has led to an abundance of keypoint detection and feature description techniques for point-clouds. While some recent methods utilize color along with geometric information, most only implicitly discuss-if not completely disregard-the information provided by scale. Scale is an inherent characteristic of any keypoint or feature that describes its physical size or region of support. Exploiting information provided by scale facilitates the processes of keypoint detection and feature description. A 3D scale-space, extended from robust and popular 2D methods, is constructed to diffuse a signal on triangulated meshes. Methods of scale-parameter estimation and a novel neighborhood definition are presented that improve the methods' robustness to noise and arbitrary mesh connectivity. Keypoint repeatability experiments show that a novel definition of the Laplace-Beltrami operator (LBO) leads to the most accurate scale-space. A point-cloud feature descriptor, popular in the robotics community and recently proposed for spacecraft relative navigation (RelNav), is modified to explicitly utilize scale in the description and matching processes. The Scaled Oriented Unique Clustered Viewpoint Feature Histogram (SOUR-CVFH) uses a cluster's scale to expedite the histogram matching procedure and increase pose estimation accuracy. SOUR-CVFH is demonstrated in experiments of (1) object recognition in both isolated and cluttered scenes and (2) simulated RelNav of a spacecraft to a asteroid.

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