Search NASA⌕ Search

SEARCH · Search NASA

Results for “feature tracking”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Automatic Feature Tracking on Small Bodies for Autonomous Approach

Abstract—The autonomous approach of a spacecraft to an asteroid or comet (a small body) relies heavily on visual feature tracking to aid in estimating relative trajectories and the properties of the small body. Feature tracking for small bodies brings several challenges, including changing lighting, poor visual texture, and a concentration of features in a small part of an image. Six existing, open-source algorithms for feature tracking were tested on a simulated dataset and compared to the ground truth in the path of features. The main finding is that none of the algorithms provide all of the desired characteristics of long feature tracks with low errors and few outliers. Instead, there is a trade-off between long feature tracks and low error. The feature-matching algorithms SIFT, and BRISK provide good error characteristics, but short feature tracks, whereas the optical flow algorithm KLT provides long feature tracks, but with many features of large error. Given the challenges in feature tracking, it is recommended to focus development on each component of a feature tracking system: detection, description, and outlier rejection.

Morrell, Benjamin J↗
NASA NTRSDate not supplied

Dense Feature Tracking of Atmospheric Winds with Deep Optical Flow

Atmospheric winds are a key physical phenomenon impacting natural hazards, energy transport, ocean currents, large-scale circulation, and ecosystem fluxes. Observing winds is a complex process and presents a large gap in NASA’s Earth Observation System. Atmospheric motion vectors (AMVs) aim to fill this gap by making numerical estimates of cloud movement between sequences of multi-spectral satellite images, tracking clouds and water vapor. Recent imaging hardware and software advancements have enabled the use of numerical optical flow techniques to produce accurate and dense vector fields outperforming traditional methods. This work presents WindFlow as the first machine learning based system for feature tracking atmospheric motion using optical flow. Due to the lack of large-scale satellite-based observations, we leverage high-resolution numerical simulations from NASA's GEOS-5 Nature Run to perform supervised learning and transfer to satellite images. We demonstrate that our approach using deep learning based optical flow scales to ultra-high-resolution images of size 2881x5760 with less than 1 m/s bias and 2.5 m/s average error. Four network and learning architectures are compared and it is found that recurrent all-pairs field transforms (RAFT) produces the lowest errors on all metrics for wind speed and direction. Results on held out numerical outputs shows RAFT's good performance in each of the spatial, temporal, and physical dimensions. A comparison between WindFlow and an operational AMV product against rawinsonde observations show that RAFT transfers across simulations and thermal infrared satellite observations. This work shows that machine learning based optical flow is an efficient approach to generating robust feature tracking for AMVs consistently over large regions.

Atmospheric winds↗
DOE OSTI2023

PyFLEXTRKR: a flexible feature tracking Python software for convective cloud analysis

Abstract. This paper describes the new open-source framework PyFLEXTRKR (Python FLEXible object TRacKeR), a flexible atmospheric feature tracking software package with specific capabilities to track convective clouds from a variety of observations and model simulations. This software can track any atmospheric 2D objects and handle merging and splitting explicitly. The package has a collection of multi-object identification algorithms, scalable parallelization options, and has been optimized for large datasets including global high-resolution data. We demonstrate applications of PyFLEXTRKR on tracking individual deep convective cells and mesoscale convective systems from observations and model simulations ranging from large-eddy resolving (∼100s m) to mesoscale (∼10s km) resolutions. Visualization, post-processing, and statistical analysis tools are included in the package. New Lagrangian analyses of convective clouds produced by PyFLEXTRKR applicable to a wide range of datasets and scales facilitate advanced model evaluation and development efforts as well as scientific discovery.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Lessons Learned from OSIRIS-Rex Autonomous Navigation Using Natural Feature Tracking

The Origins, Spectral Interpretation, Resource Identification, Security-Regolith Explorer (Osiris-REx) spacecraft is scheduled to launch in September, 2016 to embark on an asteroid sample return mission. It is expected to rendezvous with the asteroid, Bennu, navigate to the surface, collect a sample (July 20), and return the sample to Earth (September 23). The original mission design called for using one of two Flash Lidar units to provide autonomous navigation to the surface. Following Preliminary design and initial development of the Lidars, reliability issues with the hardware and test program prompted the project to begin development of an alternative navigation technique to be used as a backup to the Lidar. At the critical design review, Natural Feature Tracking (NFT) was added to the mission. NFT is an onboard optical navigation system that compares observed images to a set of asteroid terrain models which are rendered in real-time from a catalog stored in memory on the flight computer. Onboard knowledge of the spacecraft state is then updated by a Kalman filter using the measured residuals between the rendered reference images and the actual observed images. The asteroid terrain models used by NFT are built from a shape model generated from observations collected during earlier phases of the mission and include both terrain shape and albedo information about the asteroid surface. As a result, the success of NFT is highly dependent on selecting a set of topographic features that can be both identified during descent as well as reliably rendered using the shape model data available. During development, the OSIRIS-REx team faced significant challenges in developing a process conducive to robust operation. This was especially true for terrain models to be used as the spacecraft gets close to the asteroid and higher fidelity models are required for reliable image correlation. This paper will present some of the challenges and lessons learned from the development of the NFT system which includes not just the flight hardware and software but the development of the terrain models used to generate the onboard rendered images.

Navigation↗
NASA NTRS2010

Real-Time Feature Tracking Using Homography

This software finds feature point correspondences in sequences of images. It is designed for feature matching in aerial imagery. Feature matching is a fundamental step in a number of important image processing operations: calibrating the cameras in a camera array, stabilizing images in aerial movies, geo-registration of images, and generating high-fidelity surface maps from aerial movies. The method uses a Shi-Tomasi corner detector and normalized cross-correlation. This process is likely to result in the production of some mismatches. The feature set is cleaned up using the assumption that there is a large planar patch visible in both images. At high altitude, this assumption is often reasonable. A mathematical transformation, called an homography, is developed that allows us to predict the position in image 2 of any point on the plane in image 1. Any feature pair that is inconsistent with the homography is thrown out. The output of the process is a set of feature pairs, and the homography. The algorithms in this innovation are well known, but the new implementation improves the process in several ways. It runs in real-time at 2 Hz on 64-megapixel imagery. The new Shi-Tomasi corner detector tries to produce the requested number of features by automatically adjusting the minimum distance between found features. The homography-finding code now uses an implementation of the RANSAC algorithm that adjusts the number of iterations automatically to achieve a pre-set probability of missing a set of inliers. The new interface allows the caller to pass in a set of predetermined points in one of the images. This allows the ability to track the same set of points through multiple frames.

Clouse, Daniel S.↗
NASA NTRSDate not supplied

Post-Flight Quantification of LOFTID Aeroshell Deflection Using Feature Tracking

The Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) was a flight demonstration of the Hypersonic Inflatable Aerodynamic Decelerator (HIAD) technology, which has the potential to enable delivery of heavy payloads to Mars, Venus, and Titan, as well as return to Earth. Unlike rigid aeroshells that are constrained by the diameter of the launch vehicle shroud, inflatable aeroshells can be deployed to a much larger drag area, thus allowing a more massive spacecraft to begin its deceleration at higher altitudes and experience less heating. On November 10, 2022, the LOFTID reentry vehicle launched aboard a United Launch Alliance Atlas V rocket to low-Earth orbit. The aeroshell was inflated to its full 6-meter diameter, and the vehicle then successfully re-entered the atmosphere, landing in the Pacific Ocean. The aeroshell was composed of seven tori bound together by high strength straps to create a 70-degree half-angle sphere-cone, and the forebody was covered with a flexible thermal protection system (FTPS) (Fig. 1). The centerbody of the vehicle housed six visual cameras. Each camera was made up of 1920 x 1080 pixels and had a field-of-view (FOV) of 85.4° x 55.6°, resulting in a resolution of less than 0.1" at all locations on the aftbody side of the aeroshell. The approximate locations of the cameras and their associated FOVs is shown in Fig. 2. The high loads experienced during flight resulted in the cone of the aeroshell deflecting. This behavior was seen during the static load testing of the aeroshell in May 2021, in which loads ranging from 1,000 to 20,000 lbf were applied, and deflections of up to ~1.7° were observed. The LOFTID team was interested in estimating the deflection of the aeroshell during the its entry into Earth's atmosphere. Before launch, 1"-diameter black circles were drawn on select structural straps for tracking with the visual cameras; the change in position of these features could then be used to calculate aeroshell deflection angle.

Hannah S. Alpert↗
NASA NTRSDate not supplied

Post-Flight Quantification of LOFTID Aeroshell Deflection Using Feature Tracking

The Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) was a flight demonstration of the Hypersonic Inflatable Aerodynamic Decelerator (HIAD) technology, which has the potential to enable delivery of heavy payloads to Mars, Venus, and Titan, as well as return to Earth. Unlike rigid aeroshells that are constrained by the diameter of the launch vehicle shroud, inflatable aeroshells can be deployed to a much larger drag area, thus allowing a more massive spacecraft to begin its deceleration at higher altitudes and experience less heating. On November 10, 2022, the LOFTID reentry vehicle launched aboard a United Launch Alliance Atlas V rocket to low-Earth orbit. The aeroshell was inflated to its full 6-meter diameter, and the vehicle then successfully re-entered the atmosphere, landing in the Pacific Ocean. The aeroshell was composed of seven tori bound together by high strength straps to create a 70-degree half-angle sphere-cone, and the forebody was covered with a flexible thermal protection system (FTPS) (Fig. 1). The centerbody of the vehicle housed six visual cameras. Each camera was made up of 1920 x 1080 pixels and had a field-of-view (FOV) of 85.4° x 55.6°, resulting in a resolution of less than 0.1" at all locations on the aftbody side of the aeroshell. The approximate locations of the cameras and their associated FOVs is shown in Fig. 2. The high loads experienced during flight resulted in the cone of the aeroshell deflecting. This behavior was seen during the static load testing of the aeroshell in May 2021, in which loads ranging from 1,000 to 20,000 lbf were applied, and deflections of up to ~1.7° were observed. The LOFTID team was interested in estimating the deflection of the aeroshell during the its entry into Earth's atmosphere. Before launch, 1"-diameter black circles were drawn on select structural straps for tracking with the visual cameras; the change in position of these features could then be used to calculate aeroshell deflection angle.

Hannah S. Alpert↗
NASA NTRS1994

High resolution cloud feature tracking on Venus by Galileo

The Venus cloud deck was monitored in February 1990 for 16 hours at 400 nanometers wavelength by the Galileo imaging system, with a spatial resolution of about 15 km and with image time separations as small as 10 minutes. Velocities are deduced by following the motion of small cloud features. In spite of the high temporal frequence is capable of being detected, no dynamical phenomena are apparent in the velocity data except the already well-known solar tides, possibly altered by the slow 4-day wave and the Hadley circulation. There is no evidence, to a level of approximately 4 m/s, of eddy or wavelike activity. The dominant size of sub-global scale albedo features is 200-500 km, and their contrast is approximately 5%. At low altitudes there are patches of blotchy, cell-like structures but at most locations the markings are streaky. The patterns are similar to those discovered by Mariner 10 and Pioneer Venus (M. J. S. Belton et al., 1976, W. B. Rossow et al., 1980). Scaling arguments are presented to argue that the mesoscale blotchy cell-like cloud patterns are caused by local dynamics driven in a shallow layer by differential absorption of sunlight. It is also argued that mesoscale albedo features are either streaky or cell-like simply depending on whether the horizontal shear of the large scale flow exceeds a certain critical value.

Toigo, Anthony↗
NASA NTRS1990

Object-oriented feature-tracking algorithms for SAR images of the marginal ice zone

An unsupervised method that chooses and applies the most appropriate tracking algorithm from among different sea-ice tracking algorithms is reported. In contrast to current unsupervised methods, this method chooses and applies an algorithm by partially examining a sequential image pair to draw inferences about what was examined. Based on these inferences the reported method subsequently chooses which algorithm to apply to specific areas of the image pair where that algorithm should work best.

Daida, Jason↗
NASA NTRS2020

Terrain Relative Navigation for Guided Descent on Titan

Titan’s dense atmosphere, low gravity, and high winds at high altitudes create descent times of >90 minutes with standard entry/descent/landing (EDL) architectures and result in large unguided landing ellipses, with 99% values of 110x110 km and 149x72 km in recent Titan lander proposals. Enabling precision landing on Titan could increase science return for the types of missions proposed to date and make additional types of landing sites accessible, opening up new possibilities for science investigations. Precision landing on Titan has unique challenges, because the hazy atmosphere makes it difficult to see the surface and because it requires guided descent with divert ranges that are one to two orders of magnitude larger than needed for other target bodies, i.e. up to on the order of 100 km. It is conceivable that such a divert capability could be provided economically by a parafoil or other steerable aerodynamic decelerator deployed several 10s of km above the surface. The long descent times lead to large inertial navigation errors, hence a need for terrain relative navigation (TRN). This would require a TRN capability that can operate at such altitudes, despite challenges of seeing the surface sufficiently clearly and of depending on map products that are two orders of magnitude lower in spatial resolution than those for Mars and airless bodies. We then develop algorithms for map matching and feature tracking with descent images and test these with synthetic images created from Cassini/Huygens data sets and our radiative transfer model. We also introduce new possibilities for TRN based on the potential to discriminate some specific types of terrain onboard in descent imagery, such as lake vs adjacent ground and dune vs interdune. We use sensor measurement noise models in simulations of state estimation with an extended Kalman filter that includes coordinates of a set of tracked features in the state vector. Case studies were done for two notional landing sites, one in a site with only dry ground and one in a Titan lake district. In both cases, the filter error model shows 3 position error at touchdown on the order of 2 km. More work is needed to validate these results with higher fidelity camera models and larger data sets, but this is very promising.

Matthies, Larry↗
NASA NTRS1977

Edge-following algorithm for tracking geological features

Sequential edge-tracking algorithm employs circular scanning to point permit effective real-time tracking of coastlines and rivers from earth resources satellites. Technique eliminates expensive high-resolution cameras. System might also be adaptable for application in monitoring automated assembly lines, inspecting conveyor belts, or analyzing thermographs, or x ray images.

Tietz, J. C.↗