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5 results for “Optical navigation”
Optical Navigation Dataset for Solar System Small Bodies
<p>This dataset has been curated for the purpose of training and evaluating a variety of local feature extractors intended for optical navigation in the proximity of Solar System small bodies (SSSBs). It aims to serve as a resource for researchers in the field and it is referenced in the related article titled "CNN-based local features for navigation near an asteroid" [1]. Additionally, the associated Python code for this dataset can be found in [2].</p> <p>The dataset is a compilation of images obtained from four distinct space missions focused on SSSBs, specifically NEAR Shoemaker (Eros) [3], Hayabusa (Itokawa) [4], Rosetta (67P/Churyumov-Gerasimenko) [5, 6], and OSIRIS-REx (Bennu) [7]. It also incorporates synthetic data generated through the utilization of a Bennu shape model [8] and OpenGL-based rendering software [9, 10]. Access to mission-specific images is available through the NASA Planetary Data System (PDS), and for the Rosetta mission, via the ESA Planetary Science Archive [11].</p> <p>The prefix <code>rot-</code> has been applied to subsets in which images have been pre-rotated to orient the SSSB's rotation axis upwards within the image frame. These subsets are primarily intended for training purposes and encompass image pairs with pixel correspondences that can be found in the <code>aflow</code> directory. Pixel correspondences are stored as 16-bit PNG images, where the G- and B-channels respectively represent the x and y image coordinates. To facilitate data compression and storage, a fixed scaling coefficient of 8 has been employed to convert the pixel correspondence float array into a 16-bit integer array to be used by the PNG compression. These pixel correspondence files can be loaded using the <code>navex.datasets.tools.load_aflow</code> function from [2].</p> <p>On the other hand, subsets designated with a <code>-d</code> postfix include depth information (<code>*.d</code> files) and are exclusively employed during the evaluation of the proposed feature extractors. The depth data is stored as scaled grayscale 16-bit integer arrays using PNG compression. A custom additional header accompanies these images, providing two 32-bit float values, namely the subtracted offset <em>v<sub>0</sub> </em>and the scale multiplier <em>s</em> utilized in the calculation of image pixel values as <em>v</em>' = (<em>v</em> - <em>v<sub>0</sub></em>)·<em>s</em>. To access the depth data as a 32-bit float array, researchers can utilize the <code>navex.datasets.tools.load_mono</code> function from [2].</p> <p>Please note that the file paths in e.g. <code>rot-cg67p-osinac.tar</code> and <code>cg67p-osinac-d.tar</code> archives are the same, so you need to either rename the extracted folder, extract them to different folders, or only extract the archive that you need.</p> <p>For clarity, it should be noted that subsets lacking the aforementioned pre- or postfixes do not contain paired images and consequently lack pixel correspondences. These subsets were exclusively used for feature extractor training in [1].</p> <p>The dataset also includes <code>*.ckpt</code> files, which are the trained feature extractor models referred to in [1]. More details about how to use them can be found in [2].</p>
Randomized Controlled Study of Optical 3D Navigated Repetitive Transcranial Magnetic Stimulation for Achalasia.
ClinicalTrials.gov study NCT06027190. IPD Sharing: Not stated. Countries: 1. Publications: 19.
Autonomous Deep-Space Optical Navigation Project
<p>Every one of the future exploration architectures being considered by NASA have, at their core, the need to rendezvous and dock with other vehicles or bodies.&nbsp; Future manned vehicles need to be able to do so with both cooperative and uncooperative vehicles and objects.&nbsp; To this end, the sensors being considered are all optical-based.&nbsp; In fact, passive sensors, such as IR cameras and visual cameras, are at the heart of any exploration architecture.&nbsp; There is a need for the onboard systems to be able to use the images provided by these sensors to rendezvous and dock/capture these objects.&nbsp; Therefore, this project will develop this capability to operate around a variety of objects, without a priori knowledge of their geometry.&nbsp; In particular, a technology called &lsquo;optical flow&rsquo; or &lsquo;visual odometry&rsquo; (VO), will be harnessed to develop a robust on-board capability using passive sensors; of course, if active sensors are available, they will be used as well. In fact, we will also apply this technique to navigating around a cratered object (such as an asteroid). This project will enhance the Agency&rsquo;s ability to operate at distant locations, without the need for ground intervention.</p><p>To date, all of the on-board navigation development performed has focused on either Low Earth Orbit (LEO) or Low Lunar Orbit (LLO).&nbsp; We seek to advance deep-space navigation technology by focusing this Internal Research and Development (IRAD) upon rendezvous and navigation in a weak gravity environment, either at Lagrangian point 2 (L2) or around an asteroid.&nbsp; Of course, this will apply to any destinations that have a strong gravity field as well.&nbsp; As well, the technology developed in this Internal Research and Development will apply to rendezvousing with vehicles such as ISS.&nbsp; We choose to focus our IRAD effort on the navigation algorithms and software for the ARCM DRO Mission, thus broadening our scope, maintaining our cutting-edge capability, and advancing US manned space exploration.&nbsp; The goal is to be flexible enough to meet the needs of the NASA vision, as it applies to any destination the Agency chooses to embark upon.</p><p>&nbsp;</p>
Modality Augmentation for Navigation: Intuitive Fusion With Optical Lightweight Device (US/CBCT)
ClinicalTrials.gov study NCT03231046. IPD Sharing: NO. Countries: 0. Publications: 0.
Modality Augmentation for Navigation: Intuitive Fusion With Optical Lightweight Device (US/MR)
ClinicalTrials.gov study NCT03232385. IPD Sharing: NO. Countries: 0. Publications: 0.
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