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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Raw_data_Batch_I: Lakeshore Drive

Date of collection: May 18, 2023 Location: Lakeshore Drive, Chicago, IL Content: “North to South” “South to North” This data set contains lidar and vision data collected along Lakeshore Drive. The “South to North” folder starts from the intersection of Lakeshore Drive and 31st Street and ends at Hollywood Towers Chicago. The “North to South” folder starts from the intersection of Lakeshore Drive and Sheridan Avenue and ends at the 31st Street intersection. The data contains the following Robot Operating System (ROS) topics: - /Central_Camera_blurred – Image flow from coaxial camera heading, vehicle front. - /Left_Camera_blurred – Image flow from left camera, 60° from central camera on the left side. - /Right_Camera_blurred – Image flow from right camera, 60° from central camera on the right side. - /camera0/camera_info – Intrinsic and distortion information of left camera. - /camera0/projection_matrix – Extrinsic matrix of left camera from lidar. - /camera2/camera_info – Intrinsic and distortion information of central camera. - /camera2/projection_matrix – Extrinsic matrix of central camera from lidar. - /camera5/camera_info – Intrinsic and distortion information of right camera. - /camera5/projection_matrix – Extrinsic matrix of right camera from lidar. - /novatel/oem7/bestpos – Latitude, longitude, and elevation information from NovAtel GNSS-INS system. - /points_raw – VLP-32 lidar point cloud. - /tf – Vehicle base frame. The data are organized into a data description file and one or more ROS .bag files, dependent on original file size. The data description file provides information about the data collection date, location, and detailed mapping, while the .bag file(s) contain the actual data. Please note that for the purpose of securing personally identifiable information, all license plates and faces included in this publication have been intentionally blurred during real-time processing. ![lakeshore drive image](lakeshore-drive.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Raw_data_Batch_I: Lisle to Waterfall Glen

Date of collection: May 11, 2023 Location: DuPage County, IL This dataset contains lidar and vision data collected between Lisle, IL, and the Waterfall Glen parking lot. The vehicle started near Cass School District 63, headed east along IL 34. The vehicle then turned south along IL 83 until Interstate 55. Finally, the vehicle turned southwest along I 55 until Exit 273A and headed toward the Waterfall Glen parking lot. The data contains the following Robot Operating System (ROS) topics: - /Central_Camera_blurred – Image flow from coaxial camera heading, vehicle front. - /Left_Camera_blurred – Image flow from left camera, 60° from central camera on the left side. - /Right_Camera_blurred – Image flow from right camera, 60° from central camera on the right side. - /camera0/camera_info – Intrinsic and distortion information of left camera. - /camera0/projection_matrix – Extrinsic matrix of left camera from lidar. - /camera2/camera_info – Intrinsic and distortion information of central camera. - /camera2/projection_matrix – Extrinsic matrix of central camera from lidar. - /camera5/camera_info – Intrinsic and distortion information of right camera. - /camera5/projection_matrix – Extrinsic matrix of right camera from lidar. - /novatel/oem7/bestpos – Latitude, longitude, and elevation information from NovAtel GNSS-INS system. - /points_raw – VLP-32 lidar point cloud. - /tf – Vehicle base frame. The data are organized into a data description file and one or more ROS .bag files, dependent on original file size. The data description file provides information about the data collection date, location, and detailed mapping, while the .bag file(s) contain the actual data. Please note that for the purpose of securing personally identifiable information, all license plates and faces included in this publication have been intentionally blurred during real-time processing. ![lisle waterfall image](lisle-waterfall.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Raw_data_Batch_I: Randall Road

Date of collection: June 3, 2022 Location: Randall Road, DuPage County, IL Content: “North to South” “South to North” This data set contains lidar and vision data collected along Randall Road in DuPage County, Illinois. The “South to North” folder starts at 1480 N. Orchard Road, Aurora, IL 60506, headed north along Randall Road until 238 N. Randall Road, St. Charles, IL 60174. The “North to South” folder starts from 238 N. Randall Road, St. Charles, IL 60174, headed south along Randall Road until 1480 N. Orchard Road, Aurora, IL. The data contains the following Robot Operating System (ROS) topics: - /Central_Camera_blurred – Image flow from coaxial camera heading, vehicle front. - /Left_Camera_blurred – Image flow from left camera, 60° from central camera on the left side. - /Right_Camera_blurred – Image flow from right camera, 60° from central camera on the right side. - /camera0/camera_info – Intrinsic and distortion information of left camera. - /camera0/projection_matrix – Extrinsic matrix of left camera from lidar. - /camera2/camera_info – Intrinsic and distortion information of central camera. - /camera2/projection_matrix – Extrinsic matrix of central camera from lidar. - /camera5/camera_info – Intrinsic and distortion information of right camera. - /camera5/projection_matrix – Extrinsic matrix of right camera from lidar. - /novatel/oem7/bestpos – Latitude, longitude, and elevation information from NovAtel GNSS-INS system. - /points_raw – VLP-32 lidar point cloud. - /tf – Vehicle base frame. The data are organized into a data description file and one or more ROS .bag files, dependent on original file size. The data description file provides information about the data collection date, location, and detailed mapping, while the .bag file(s) contain the actual data. Please note that for the purpose of securing personally identifiable information, all license plates and faces included in this publication have been intentionally blurred during real-time processing. ![randall road image](randall-road.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Raw_data_Batch_I: State Street

Date of collection: May 18, 2023 Location: State Street, Chicago, IL This data set contains lidar and vision data collected along State Street. The vehicle started from outside of the McCormick Tribune Campus Center at the Illinois Institute of Technology’s Mies Campus and headed north along State Street, until the north end of State Street in the Gold Coast. The data contains the following Robot Operating System (ROS) topics: - /Central_Camera_blurred – Image flow from coaxial camera heading, vehicle front. - /Left_Camera_blurred – Image flow from left camera, 60° from central camera on the left side. - /Right_Camera_blurred – Image flow from right camera, 60° from central camera on the right side. - /camera0/camera_info – Intrinsic and distortion information of left camera. - /camera0/projection_matrix – Extrinsic matrix of left camera from lidar. - /camera2/camera_info – Intrinsic and distortion information of central camera. - /camera2/projection_matrix – Extrinsic matrix of central camera from lidar. - /camera5/camera_info – Intrinsic and distortion information of right camera. - /camera5/projection_matrix – Extrinsic matrix of right camera from lidar. - /novatel/oem7/bestpos – Latitude, longitude, and elevation information from NovAtel GNSS-INS system. - /points_raw – VLP-32 lidar point cloud. - /tf – Vehicle base frame. The data are organized into a data description file and one or more ROS .bag files, dependent on original file size. The data description file provides information about the data collection date, location, and detailed mapping, while the .bag file(s) contain the actual data. Please note that for the purpose of securing personally identifiable information, all license plates and faces included in this publication have been intentionally blurred during real-time processing. ![state street image](state-street.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1994

High-resolution imaging with a tip-tilt Cassegrain secondary

A prototype adaptive secondary mirror has been developed on a 2.3-m telescope to stabilize rapid motion caused by atmospheric turbulence. The instantaneous position of an image's centroid, or brightest speckle, is measured by a near-infrared camera system which enables the tip-tilt mirror to recenter the image. Correction is accomplished at closed-loop frequencies of less than or = 100 Hz on guide stars with K (2.2 micrometers) magnitudes less than or = 8. Image motion is reduced to less than 0.1 sec rms, removing over 90% of low-frequency (f less than or = 5 Hz) tilt power, leaving less than or = 1 rad exp 2 mean square wave front tilt phase error. In seeing conditions where D/r sub o approximately = 4, long exposures in the H band (1.6 micrometers) exhibit a nearly diffraction-limited resolution of 0.19 sec full width at half maximum (FWHM) (Strehl ratio aprroximately = 0.14)-a factor of 4 improvement over uncorrected images. Reduction of the present static optical errors will improve the Strehl ratio another factor of 2, leading to approximately 70% of the maximum possible Strehl ratio.

Close, L. M.↗
DOE OSTI2026

Improving 3D reconstruction quality for root phenotyping: assessing the impact of camera calibration and imaging parameters

Arate 3D reconstruction is essential for high-throughput plant phenotyping, particularly for studying complex structures such as root systems. While photogrammetry and Structure from Motion (SfM) techniques have become widely used for 3D root imaging, the camera settings used are often underreported in studies, and the impact of camera calibration on model accuracyccu remains largely underexplored in plant science. In this study, we systematically evaluate the effects of focus, aperture, exposure time, and gain settings on the quality of 3D root models made with a multi-camera scanning system. We show through a series of experiments that calibration significantly improves model quality, with focus misalignment and shallow depth of field (DoF) being the most important factors affecting reconstruction accuracy. Our results further show that proper calibration has a greater effect on reducing noise than filtering it during post-processing, emphasizing the importance of optimizing image acquisition rather than relying solely on computational corrections. This work improves the repeatability and accuracy of 3D root imaging for phenotyping pipelines by giving useful calibration guidelines. This leads to better trait quantification for use in crop research and plant breeding in downstream analysis.

3D reconstruction↗
DOE OSTI2024

Semi-automatic image annotation using 3D LiDAR projections and depth camera data

Efficient image annotation is necessary to utilize deep learning object recognition neural networks in nuclear safeguards, such as for the detection and localization of target objects like nuclear material containers (NMCs). This capability can help automate the inventory accounting of different types of NMCs within nuclear storage facilities. The conventional manual annotation process is labor-intensive and time-consuming, hindering the rapid deployment of deep learning models for NMC identifications. This paper introduces a novel semi-automatic method for annotating 2D images of nuclear material containers (NMCs) by combining 3D light detection and ranging (LiDAR) data with color and depth camera images collected from a handheld scan system. The annotation pipeline involves an operator manually marking new target objects on a LiDAR-generated map, and projecting these 3D locations to images, thereby automatically creating annotations from the projections. The semi-automatic approach significantly reduces manual efforts and the expertise in image annotation that is required to perform the task, allowing deep learning models to be trained on-site within a few hours. The paper compares the performance of models trained on datasets annotated through various methods, including semi-automatic, manual, and commercial annotation services. The evaluation demonstrates that the semi-automatic annotation method achieves comparable or superior results, with a mean average precision (mAP) above 0.9, showcasing its efficiency in training object recognition models. Additionally, the paper explores the application of the proposed method to instance segmentation, achieving promising results in detecting multiple types of NMCs in various formations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques

Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine‐learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine‐learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density‐based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image‐derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle‐related labels compared to the camera‐derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short‐term average/long‐term average method and k‐means clustering. Our results suggest that seismic data, when analyzed with machine‐learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.

Chai, Chengping [Oak Ridge National Laboratory (OR↗
NASA NTRS1971

Digital image processing for the rectification of television camera distortions.

All television systems introduce distortions into the imagery they record which influence the results of quantitative photometric and geometric measurements. Digital computer techniques provide a powerful approach to the calibration and rectification of these systematic effects. Nonlinear as well as linear problems can be attacked with flexibility and precision. Methods which have been developed and applied for the removal of structured system noises and the correction of photometric, geometric, and resolution distortions in vidicon systems are briefly described. Examples are given of results derived primarily from the Mariner Mars 1969 television experiment.

Rindfleisch, T. C.↗
NASA NTRS1979

The ability of the space telescope to detect extra-solar planetary systems

The space telescope can plan a key role in searching for and investigating the contents of extra-solar planetary systems. For about 90 nearby stars, positional variations due to major planets would be well within the astrometric capability of the wide-field/planetary camera system. Since the centroids of star images will be determined to within a milliarcsecond down to 22d magnitude, there will be an abundance of reference stars at very small angular distances from each planetary system candidate, and they will have small enough motions of their own to provide a reference frame of the stability required.

Baum, W. A.↗
NASA NTRS1980

The mosaics of Mars: As seen by the Viking Lander cameras

The mosaics and derivative products produced from many individual high resolution images acquired by the Viking Lander Camera Systems are described: A morning and afternoon mosaic for both cameras at the Lander 1 Chryse Planitia site, and a morning, noon, and afternoon camera pair at Utopia Planitia, the Lander 11 site. The derived products include special geometric projections of the mosaic data sets, polar stereographic (donut), stereoscopic, and orthographic. Contour maps and vertical profiles of the topography were overlaid on the mosaics from which they were derived. Sets of stereo pairs were extracted and enlarged from stereoscopic projections of the mosaics.

Levinthal, E. C.↗
NASA NTRS1984

Investigation of light source and scattering medium related to vapor-screen flow visualization in a supersonic wind tunnel

Methods for increasing the radiant in light sheets used for vapor screen set-ups were investigated. Both high-pressure mercury arc lamps and lasers were considered. Pulsed operation of the air-cooled 1-kW lamps increased the light output but decreased reliability. An ellipsoidal mirror improved the output of the air-cooled lamps by concentrating the light but increased the complexity of the housing. Water-cooled-4-kW lamps coupled with high-aperture Fresnel lenses provided reasonable improvements over the air-cooled lamps. Fanned laser beams measurements of scattered light versus dew point made in conjunction with successful attempts to control the fluid injection. A number of smoke generators are described and test results comparing smoke and vapor screens are shown. Finally, one test included a periscope system to relay the image to a camera outside the flow.

Snow, W. L.↗
NASA NTRS1988

Planar and finger-shaped optical tactile sensors for robotic applications

Progress is described regarding the development of optical tactile sensors specifically designed for application to dexterous robotics. These sensors operate on optical principles involving the frustration of total internal reflection at a waveguide/elastomer interface and produce a grey-scale tactile image that represents the normal (vertical) forces of contact. The first tactile sensor discussed is a compact, 32 x 32 planar sensor array intended for mounting on a parallel-jaw gripper. Optical fibers were employed to convey the tactile image to a CCD camera and microprocessor-based image analysis system. The second sensor had the shape and size of a human fingertip and was designed for a dexterous robotic hand. It contained 256 sensing sites (taxels) distributed in a dual-density pattern that included a tactile fovea near the tip measuring 13 x 13 mm and containing 169 taxels. The design and construction details of these tactile sensors are presented, in addition to photographs of tactile imprints.

Begej, Stefan↗
NASA NTRS1991

Significant reduction in arc frequency biased solar cells: Observations, diagnostics, and mitigation technique(s)

A variety of experiments were performed which identify key factors contributing to the arcing of negatively biased high voltage solar cells. These efforts have led to reduction of greater than a factor of 100 in the arc frequency of a single cell following proper remediation procedures. Experiments naturally lead to and focussed on the adhesive/encapsulant that is used to bond the protective cover slip to the solar cell. An image-intensified charge coupled device (CCD) camera system recorded UV emission from arc events which occurred exclusively along the interfacial edge between the cover slip and the solar cell. Microscopic inspection of this interfacial region showed a bead of encapsulant along this entire edge. Elimination of this encapsulant bead reduced the arc frequency by two orders of magnitude. Water contamination was also identified as a key contributor which enhances arcing of the encapsulant bead along the solar cell edge. Spectrally resolved measurements of the observable UV light shows a feature assignable to OH(A-X) electronic emission, which is common for water contaminated discharges. Experiments in which the solar cell temperature was raised to 85 C showed a reduced arcing frequency, suggesting desorption of H2O. Exposing the solar cell to water vapor was shown to increase the arcing frequency. Clean dry gases such as O2, N2, and Ar show no enhancement of the arcing rate. Elimination of the exposed encapsulant eliminates any measurable sensitivity to H2O vapor.

Upschulte, B. L.↗
NASA NTRS1989

Holographic diagnostics of breakup phenomena of impinging jets for liquid rocket motor applications

The use of pulsed laser holography in analyzing the atomization of impinging jets is evaluated using a holographic camera system capable of recording a 3D image of the atomization process in 3 x 10 exp -8 seconds. The holocamera and the experimental configuration and procedure are described in detail. Two nozzles were fabricated to produce 1-mm diameter liquid water jets that impinged at 60 and 90 deg. Holograms were obtained for each nozzle at liquid velocities of 20 and 40 m/s. Analysis of the holograms revealed the evolution of the jets from the plane of emergence through the region of droplet formation. Features of interest included the growth of disturbances prior to the breakup region and the effect of these disturbances on the formation of ligaments and liquid globules. Preliminary analyses show that waves of atomized liquid formed after impingement are closely correlated to waves measured on the jets, leading to the possibility of controlling the atomization process at the nozzle.

Hess, C. F.↗
NASA NTRS1994

Vision based object pose estimation for mobile robots

Mobile robot navigation using visual sensors requires that a robot be able to detect landmarks and obtain pose information from a camera image. This paper presents a vision system for finding man-made markers of known size and calculating the pose of these markers. The algorithm detects and identifies the markers using a weighted pattern matching template. Geometric constraints are then used to calculate the position of the markers relative to the robot. The selection of geometric constraints comes from the typical pose of most man-made signs, such as the sign standing vertical and the dimensions of known size. This system has been tested successfully on a wide range of real images. Marker detection is reliable, even in cluttered environments, and under certain marker orientations, estimation of the orientation has proven accurate to within 2 degrees, and distance estimation to within 0.3 meters.

Wu, Annie↗
NASA NTRS1999

AFM Studies of Lunar Soils and Application to the Mars 2001 Mission

The upcoming Mars 01 mission will carry an Atomic Force Microscope (AFM) as part of the Mars Environmental Compatibility Assessment (MECA) instrument. By operating in a tapping mode, the AFM is capable of sub-nanometer resolution in three dimensions and can distinguish between substances of different compositions by employing phase contrast imaging. To prepare for the Mars 01 mission, we are testing the AFM on a lunar soil to determine its ability to define particle shapes and sizes and grain-surface textures. The test materials are from the Apollo 17 soil 79221, which is a mixture of agglutinates, impact and volcanic beads, and mare and highland rock and mineral fragments. The majority of the lunar soil particles are less than 100 microns in size, comparable to the sizes estimated for martian dust. We have used the AFM to examine several different soil particles at various resolutions. The instrument has demonstrated the ability to identify parallel ridges characteristic of twinning on a 150 micron plagioclase feldspar particle. Extremely small (10-100 nanometer) adhering particles are visible on the surface of the feldspar grain, and they appear elongate with smooth surfaces. Phase contrast imaging of the nanometer particles shows several compositions to be present. When the AFM was applied to a 100 micron glass spherule, it was possible to define an extremely smooth surface; this is in clear contrast to results from a basalt fragment which exhibited a rough surface texture. Also visible on the surface of the glass spherule were chains of 100 nanometer and smaller impact melt droplets. For the '01 Mars mission, the AFM is intended to define the size and shape distributions of soil particles, in combination with the NMCA optical microscope system and images from the Robot Arm Camera (RAC). These three data sets will provide a means of assessing potentially hazardous soil and dust properties. The study that we have conducted on the lunar soils now suggests that the NMCA experiment will be able to define grain transport and weathering processes. For example, it should be possible to determine if Martian grains have been subjected to aeolian or water transport, volcanic activity, impact melting processes, in-situ weathering, and a host of other processes. Additionally, textural maturity could be assessed (via freshness and form of fracture patterns and grain shapes). Thus, the AFM has the potential to shed new light on Martian surface processes by adding the submicroscopic dimension to planetary investigations.

Weitz, C. M.↗