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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

ULTRA: Underwater Localization for Transit and Reconnaissance Autonomy

This software addresses the issue of underwater localization of unmanned vehicles and the inherent drift in their onboard sensors. The software gives a 2 to 3 factor of improvement over the state-of-the-art underwater localization algorithms. The software determines the localization (position, heading) of an AUV (autonomous underwater vehicle) in environments where there is no GPS signal. It accomplishes this using only the commanded position, onboard gyros/accelerometers, and the bathymetry of the bottom provided by an onboard sonar system. The software does not rely on an onboard bathymetry dataset, but instead incrementally determines the position of the AUV while mapping the bottom. In order to enable long-distance underwater navigation by AUVs, a localization method called ULTRA uses registration of the bathymetry data products produced by the onboard forward-looking sonar system for hazard avoidance during a transit to derive the motion and pose of the AUV in order to correct the DR (dead reckoning) estimates. The registration algorithm uses iterative point matching (IPM) combined with surface interpolation of the Iterative Closest Point (ICP) algorithm. This method was used previously at JPL for onboard unmanned ground vehicle localization, and has been optimized for efficient computational and memory use.

Huntsberger, Terrance L.↗
NASA NTRS2014

Vision Algorithm for the Solar Aspect System of the HEROES Mission

This work covers the design and test of a machine vision algorithm for generating high-accuracy pitch and yaw pointing solutions relative to the sun for the High Energy Replicated Optics to Explore the Sun (HEROES) mission. It describes how images were constructed by focusing an image of the sun onto a plate printed with a pattern of small fiducial markers. Images of this plate were processed in real time to determine relative position of the balloon payload to the sun. The algorithm is broken into four problems: circle detection, fiducial detection, fiducial identification, and image registration. Circle detection is handled by an "Average Intersection" method, fiducial detection by a matched filter approach, identification with an ad-hoc method based on the spacing between fiducials, and image registration with a simple least squares fit. Performance is verified on a combination of artificially generated images, test data recorded on the ground, and images from the 2013 flight

Solar Aspect↗
NASA NTRS2014

Vision Algorithm for the Solar Aspect System of the HEROES Mission

This work covers the design and test of a machine vision algorithm for generating high-accuracy pitch and yaw pointing solutions relative to the sun for the High Energy Replicated Optics to Explore the Sun (HEROES) mission. It describes how images were constructed by focusing an image of the sun onto a plate printed with a pattern of small fiducial markers. Images of this plate were processed in real time to determine relative position of the balloon payload to the sun. The algorithm is broken into four problems: circle detection, fiducial detection, fiducial identification, and image registration. Circle detection is handled by an Average Intersection method, fiducial detection by a matched filter approach, identification with an ad-hoc method based on the spacing between fiducials, and image registration with a simple least squares fit. Performance is verified on a combination of artificially generated images, test data recorded on the ground, and images from the 2013 flight.

HEROES↗
NASA NTRS2013

Characterization of ASTER GDEM Elevation Data over Vegetated Area Compared with Lidar Data

Current researches based on areal or spaceborne stereo images with very high resolutions (less than 1 meter) have demonstrated that it is possible to derive vegetation height from stereo images. The second version of the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) is a state-of-the-art global elevation data-set developed by stereo images. However, the resolution of ASTER stereo images (15 meters) is much coarser than areal stereo images, and the ASTER GDEM is compiled products from stereo images acquired over 10 years. The forest disturbances as well as forest growth are inevitable in 10 years time span. In this study, the features of ASTER GDEM over vegetated areas under both flat and mountainous conditions were investigated by comparisons with lidar data. The factors possibly affecting the extraction of vegetation canopy height considered include (1) co-registration of DEMs; (2) spatial resolution of digital elevation models (DEMs); (3) spatial vegetation structure; and (4) terrain slope. The results show that accurate co-registration between ASTER GDEM and the National Elevation Dataset (NED) is necessary over mountainous areas. The correlation between ASTER GDEM minus NED and vegetation canopy height is improved from 0.328 to 0.43 by degrading resolutions from 1 arc-second to 5 arc-seconds and further improved to 0.6 if only homogenous vegetated areas were considered.

vegetation canopy↗
NASA NTRS2015

Developing Data Citations from Digital Object Identifier Metadata

NASA's Earth Science Data and Information System (ESDIS) Project has been processing information for the registration of Digital Object Identifiers (DOI) for the last five years of which an automated system has been in operation for the last two years. The ESDIS DOI registration system has registered over 2000 DOIs with over 1000 DOIs held in reserve until all required information has been collected. By working towards the goal of assigning DOIs to the 8000+ data collections under its management, ESDIS has taken the first step towards facilitating the use of data citations with those products. Jeanne Behnke, ESDIS Deputy Project Manager has reviewed and approved the poster.

digital object idenifiers↗
NASA NTRS2016

High Resolution Image Reconstruction from Projection of Low Resolution Images DIffering in Subpixel Shifts

In this paper, we demonstrate a simple algorithm that projects low resolution (LR) images differing in subpixel shifts on a high resolution (HR) also called super resolution (SR) grid. The algorithm is very effective in accuracy as well as time efficiency. A number of spatial interpolation techniques using nearest neighbor, inverse-distance weighted averages, Radial Basis Functions (RBF) etc. used in projection yield comparable results. For best accuracy of reconstructing SR image by a factor of two requires four LR images differing in four independent subpixel shifts. The algorithm has two steps: i) registration of low resolution images and (ii) shifting the low resolution images to align with reference image and projecting them on high resolution grid based on the shifts of each low resolution image using different interpolation techniques. Experiments are conducted by simulating low resolution images by subpixel shifts and subsampling of original high resolution image and the reconstructing the high resolution images from the simulated low resolution images. The results of accuracy of reconstruction are compared by using mean squared error measure between original high resolution image and reconstructed image. The algorithm was tested on remote sensing images and found to outperform previously proposed techniques such as Iterative Back Projection algorithm (IBP), Maximum Likelihood (ML), and Maximum a posterior (MAP) algorithms. The algorithm is robust and is not overly sensitive to the registration inaccuracies.

0↗
NASA NTRS2015

A New Lunar Digital Elevation Model from the Lunar Orbiter Laser Altimeter and SELENE Terrain Camera

We present an improved lunar digital elevation model (DEM) covering latitudes within +/-60 deg, at a horizontal resolution of 512 pixels per degree ( approx.60 m at the equator) and a typical vertical accuracy approx.3 to 4 m. This DEM is constructed from approx.4.5 ×10(exp 9) geodetically-accurate topographic heights from the Lunar Orbiter Laser Altimeter (LOLA) onboard the Lunar Reconnaissance Orbiter, to which we co-registered 43,200 stereo-derived DEMs (each 1 deg×1 deg) from the SELENE Terrain Camera (TC) ( approx.10(exp 10) pixels total). After co-registration, approximately 90% of the TC DEMs show root-mean-square vertical residuals with the LOLA data of < 5 m compared to approx.50% prior to co-registration. We use the co-registered TC data to estimate and correct orbital and pointing geolocation errors from the LOLA altimetric profiles (typically amounting to < 10 m horizontally and < 1 m vertically). By combining both co-registered datasets, we obtain a near-global DEM with high geodetic accuracy, and without the need for surface interpolation. We evaluate the resulting LOLA + TC merged DEM (designated as "SLDEM2015") with particular attention to quantifying seams and crossover errors.

Barker, M. K.↗
NASA NTRS2018

GOES-16 ABI Navigation Assessment

The US Geostationary Operational Environmental Satellite – R Series (GOES-R) was launched on November 19, 2016and was designated GOES-16 upon reaching geostationary orbit ten days later. After checkout and calibration, GOES-16 was relocated to its operational location of 75.2 degrees west and officially became GOES East on December 18, 2017. The Advanced Baseline Imager (ABI) is the primary instrument on the GOES-R series for imaging Earth's surface and atmosphere to significantly improve the detection and observation of severe environmental phenomena. A team supporting the GOES-R Flight Project at NASA's Goddard Space Flight Center developed algorithms and software for independent verification of ABI Image Navigation and Registration (INR), which became known as the INR Performance Assessment Tool Set (IPATS). In this paper, we will briefly describe IPATS on top concept level, and then introduce the Landsat chips, chip registration algorithms, and how IPATS measurements are filtered. We present GOES-16 navigation (NAV) errors from flight data from January 2017 to May 2018. The results show a) IPATS characterized INR variations throughout the post-launch test phase; and b) ABI INR has improved over time as post-launch tests were performed and corrections applied. Finally, we will describe how estimated NAV errors have been used to assess and understand satellite attitude anomalies and scale errors etc. This paper shows that IPATS is an effective tool for assessing and improving GOES-16 ABI INR and is also useful for INR long-term monitoring.

GOES-R↗
NASA NTRS2018

The Harmonized Landsat and Sentinel-2 Surface Reflectance Data Set

The Harmonized Landsat and Sentinel-2 (HLS) project is a NASA initiative aiming to produce a VirtualConstellation (VC) of surface reflectance (SR) data acquired by the Operational Land Imager (OLI) and MultiSpectral Instrument (MSI) aboard Landsat 8 and Sentinel-2 remote sensing satellites, respectively. The HLS products are based on a set of algorithms to obtain seamless products from both sensors (OLI and MSI): atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, bidirectional reflectance distribution function normalization and spectral bandpass adjustment. Three products are derivedfrom the HLS processing chain: (i) S10: full resolution MSI SR at 10 m, 20 m and 60 m spatial resolutions; (ii)S30: a 30 m MSI Nadir BRDF (Bidirectional Reflectance Distribution Function)-Adjusted Reflectance (NBAR);(iii) L30: a 30 m OLI NBAR. All three products are processed for every Level-1 input products from Landsat 8/OLI (L1T) and Sentinel-2/MSI (L1C). As of version 1.3, the HLS data set covers 10.35 million km2 and spans from first Landsat 8 data (2013); Sentinel-2 data spans from October 2015. The L30 and S30 show a good consistency with coarse spatial resolution products, in particular MODIS Collection 6 MCD09CMG products (overall deviations do not exceed 11%) that are used as a reference for quality assurance. The spatial co-registration of the HLS is improved compared to original Landsat 8 L1T and Sentinel 2A L1C products, for which misregistration issues between multi-temporal data are known. In particular, the resulting computed circular errors at 90% for the HLS product are 6.2 m and 18.8 m, for S10 and L30 products, respectively. The main known issue of the current data set remains the Sentinel-2 cloud mask with many cloud detection omissions. The cross-comparison with MODIS was used to flag products with most evident non-detected clouds. A time series outlier filtering approach is suggested to detect remaining clouds. Finally, several time series are presented to highlight the high potential of the HLS data set for crop monitoring.

Landsat Sentinel-2↗
NASA NTRS2019

Orbital Path and Spacecraft Attitude Correction for the MODIS Lunar Spatial Characterization

For the Moderate Resolution Imaging Spectroradiometer(MODIS) on the Terra and Aqua platforms, regularlyscheduled lunar observations using spacecraft roll maneuvershave been used extensively for sensor characterization. Whilethe primary purpose of these observations is for radiometriccalibration of the reflective solar bands, they have also beenleveraged for a number of other sensor performance assessments,such as the band-to-band (BBR) and detector-to-detector (DDR)spatial registration. The spatial registration calculations arecomplicated by the fact that the Moon does not move in astraight path across the sensor field-of-view (FOV). This path isdetermined by the relative orbital motion between the spacecraftand the Moon and the instrument attitude error that resultsfrom the roll maneuver. In this work, we develop a correctionfor the MODIS lunar spatial characterization measurements bycalculating the predicted path of the Moon across the sensor FOVusing spacecraft and lunar ephemeris data to model the relativeorbital motion between the spacecraft and the Moon along withspacecraft attitude error data acquired during the roll maneuver.The difference between measured and predicted positions of theMoon in the MODIS FOV can be used to calculate the BBRand DDR results. Since the predicted path across the FOV willbe the same for each band, the BBR results will be minimallyaffected. However, we will show that the along-scan spread inthe DDR can be significantly reduced, which results in a muchgreater consistency throughout the full mission for both Aquaand Terra MODIS.

Truman Wilson↗
NASA NTRS2019

Validation and Uncertainty Analysis of Stereo Time-Resolved PIV Measurements for Boundary-Layer Transition Research

An uncertainty analysis was performed on the stereo time-resolved particle image velocimetry measurements that were obtained in the boundary-layer of a swept wing model. Several different approaches were undertaken to estimate the uncertainty of the PIV measurements and to understand how that uncertainty propagates through to the results of interest. It was found that the stereo registration error, which is often neglected in stereo- PIV uncertainty analysis, can cause significant errors in the measured amplitude of the stationary cross ow instability due to the large spanwise velocity gradients that occur in the ow. The self-calibrations do not always adequately correct for the stereo-registration error. A simple approach is demonstrated for estimating the error due to a misalignment of the calibration target and laser plane. This approach was found to be very successful at predicting the error in stationary cross ow amplitude caused by the misalignment. The time-resolved PIV results are also compared to previously-acquired hotwire data. The TRPIV technique is successful at measuring the same types of unsteady instabilities as the hotwire, and thus does not appear to have any adverse e ect on the transition process due to the introduction of particles into the ow. It was also found that a larger interrogation window size is beneficial for reducing the noise of the TRPIV results.

Eppink, Jenna L.↗
NASA NTRS2022

Registering Articulated Objects With Human-in-the-loop Corrections

Remotely programming robots to execute tasks often relies on registering objects of interest in the robot’s environment. Frequently, these tasks involve articulating objects such as opening or closing a valve. However, existing human-in-the-loop methods for registering objects do not consider articulations and the corresponding impact to the geometry of the object, which can cause the methods to fail. In this work, we present an approach where the registration system attempts to automatically determine the object model, pose, and articulation for user-selected points using a nonlinear iterative closest point algorithm. When the automated fitting is incorrect, the operator can iteratively intervene with corrections after which the system will refit the object. We present an implementation of our fitting procedure and evaluate it with a user study that shows that it can improve user performance, in measures of time on task and task load, ease of use, and usefulness compared to a manual registration approach. We also present a situated example that demonstrates the integration of our method in an end-to-end system for articulating a remote valve.

teleoperation↗
NASA NTRSDate not supplied

Ten Year Ozone Profile Record From Suomi NPP OMPS Limb Profiler

In this study, we evaluate a decadal ozone profile record derived from Suomi NPP Ozone Mapping and Profiler Suite (OMPS) Limb Profiler (LP). The data from OMPS LP were re-processed with the new retrieval algorithm from 2012 to present. The new reprocessing includes adjustments in both Level 1 and Level 2 algorithms. The sensor’s altitude registration, updates in radiometric calibrations, under-sampling and stray light corrections were addressed in Level 1 processing. The new retrieval algorithm (version 2.7) combines measurements from UV and VIS parts of the spectra to retrieve a single vertical ozone profile between 12.5 km (or cloud tops) and 57.5 km, unlike to previous version 2.5 where ozone profiles were retrieved independently from UV and VIS. To improve ozone retrievals in the Upper Troposphere Lower Stratosphere (UTLS) region, we implemented a new aerosol correction that assumes a gamma-function particle size distribution. In addition, ozone and nitrogen dioxide cross sections and climatologies were updated. We discuss sensitivity of the ozone retrievals to implemented changes in Level 1 and Level 2 algorithms. We focus our analysis on evaluation of the stability of the 10-year record from OMPS LP. We show results from the internal methods for characterizing the stability of the LP altitude registration and compare the LP reflectivity and ozone with the similar quantities measured by OMPS nadir sensors. Finally, we will show results from comparisons with independent satellite (MLS and SAGE III) and ozone sonde measurements.

Ozone↗
NASA NTRSDate not supplied

Assessment of High Resolution Commercial Satellite Geolocation Accuracy

Commercial companies such as Maxar, PlanetScope, and BlackSky have launched many satellites with revisit times ranging from two hours to one day and have built large archives of high resolution (1-3 m) Earth observing data. The high temporal resolution and global coverage of these satellites makes commercial satellite images ideal for scientists studying rapidly changing processes such as flooding and fires. A key step in assessing changes in these images is co-registration of various images to each other. High geolocation accuracy (at most 0.5 pixels of offset) allows for easy co-registration of images across different times and sensors. Here, we assess the latest PlanetScope imagery and evaluate it for geolocation accuracy. For our assessment, we compare the target image to a reference image with known geolocation accuracy (WorldView imagery) and determine the offset between these images by shifting them to maximize their Pearson Cross-Correlation (PCC) value. Offsets required to maximize the PCC give the geolocation accuracy of the target image relative to the reference image. Previously, our global assessment of Planet data revealed large variability in geolocation accuracy from one continent to another. The measured root mean squared errors (RMSEs) range from 5.4 m in North America to 14.9 m in Africa. We have updated this previous assessment to include both their newest SuperDove series as well as a more robust assessment of the temporal stability of PlanetScope's geolocation accuracy.

Alana G. Semple↗
NASA NTRSDate not supplied

Geometric Assessment of PlanetScope Imagery

Commercial companies such as Maxar, Planet Labs, and BlackSky have built large archives of high resolution (1-3 m) Earth observing data. The high temporal resolution and global coverage of these satellites makes commercial satellite images ideal for scientists studying rapidly changing processes such as flooding and fires. The high spatial resolution allows scientists to push studies to a fine scale such as monitoring agricultural growth experiments or detecting vehicles for traffic management. Scientists using these data need to know geolocation accuracy, band-to-band registration (BBR), and pixel footprint size to best determine appropriate applications as well as what extra steps they will need to take to use the data. Here, we find those key geometric properties for images from Planet Labs’ SuperDove constellation. Geolocation accuracy is assessed relative to WorldView images at globally distributed cities, band-to-band registration (BBR) is examined for every image we acquired (200+ images), and pixel footprint sizes are calculated from edge responses of the images over Cal/Val sites in China, India, or USA both near launch time and 1+ years after launch. We find that SuperDove geolocation is highly self-consistent with average global offsets among SuperDove images of 3.80 m (1.27 pixels). Despite this self-consistency, relative geolocation accuracy (with WorldView images as reference) varies by location from 3.20 m – 28.09 m mean offset. SuperDove BBR is sub-pixel and varies by band, with a mean radial offset relative to red band varying from 0.39 - 1.13 m. SuperDove image footprint size improves after 1+ years in orbit, with an average footprint size of 3.41 pixels (10.24 m) soon after launch and 3.23 pixels (9.7 m) 1+ years after launch. The BBR and self-consistency offsets are relatively small, given the large footprint sizes. A similarly extensive investigation is in progress for Planet Labs’ Dove-R series.

Alana G. Semple↗
DOE OSTI2022

Delaunay walk for fast nearest neighbor: accelerating correspondence matching for ICP

Point set registration algorithms such as Iterative Closest Point (ICP) are commonly utilized in time-constrained environments like robotics. Finding the nearest neighbor of a point in a reference 3D point set is a common operation in ICP and frequently consumes at least 90% of the computation time. We introduce a novel approach to performing the distance-based nearest neighbor step based on Delaunay triangulation. This greedy algorithm finds the nearest neighbor of a query point by traversing the edges of the Delaunay triangulation created from a reference 3D point set. Our work integrates the Delaunay traversal into the correspondences search of ICP and exploits the iterative aspect of ICP by caching previous correspondences to expedite each iteration. An algorithmic analysis and comparison is conducted showing an order of magnitude speedup for both serial and vector processor implementation.

3d point cloud processing↗
DOE OSTI2020

Report on the deployment of the National Geothermal Data System 2.0

This reports includes a video description of recent upgrades and changes to the National Geothermal Data System (geothermaldata.org) and a text report of its relevant security upgrades. Improvements include a new operating system, implementation of HTTPS, implementation of a standard firewall, PostgreSQL upgrades, an ESRI ArcGIS server, new registration policies, and a non-public API.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

The State of Electric Vehicle Adoption in Colorado for Multifamily versus Single-Family Dwellings: A Methodology for Quantifying Deviation from Parity

Given that electric vehicle adoption is well underway, the spatial distribution of electric vehicle owners by housing type—single-family or multifamily— shows whether parity (equal adoption rates) is being achieved or to what extent adoption by housing type is over or undersaturated (i.e., over- or under-adoption). We use a proprietary dataset of vehicle registrations with modeled housing type to analyze saturation ratios in Colorado in 2022. We found significant single-family oversaturation and multifamily undersaturation in 14% and 23% of ZIP codes, respectively, suggesting Colorado can still mitigate disparities in electric vehicle adoption by housing type through accessible vehicles and charging.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗