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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Model Development and Testing for THEMIS Controlled Mars Mosaics

As part of our work [1] to develop techniques and procedures to create regional and eventually global THEMIS mosaics of Mars, we are developing algorithms and software to photogrammetrically control THEMIS IR line scanner camera images. We have found from comparison of a limited number of images to MOLA digital image models (DIMs) [2] that the a priori geometry information (i.e. SPICE [3]) for THEMIS images generally allows their relative positions to be specified at the several pixel level (e.g. approx.5 to 13 pixels). However a need for controlled solutions to improve this geometry to the sub-pixel level still exists. Only with such solutions can seamless mosaics be obtained and likely distortion from spacecraft motion during image collection removed at such levels. Past experience has shown clearly that such mosaics are in heavy demand by users for operational and scientific use, and that they are needed over large areas or globally (as opposed to being available only on a limited basis via labor intensive custom mapping projects). Uses include spacecraft navigation, landing site planning and mapping, registration of multiple data types and image sets, registration of multispectral images, registration of images with topographic information, recovery of thermal properties, change detection searches, etc.

Archinal, B. A.↗
NASA NTRS2012

Projection of Stabilized Aerial Imagery Onto Digital Elevation Maps for Geo-Rectified and Jitter-Free Viewing

As imagery is collected from an airborne platform, an individual viewing the images wants to know from where on the Earth the images were collected. To do this, some information about the camera needs to be known, such as its position and orientation relative to the Earth. This can be provided by common inertial navigation systems (INS). Once the location of the camera is known, it is useful to project an image onto some representation of the Earth. Due to the non-smooth terrain of the Earth (mountains, valleys, etc.), this projection is highly non-linear. Thus, to ensure accurate projection, one needs to project onto a digital elevation map (DEM). This allows one to view the images overlaid onto a representation of the Earth. A code has been developed that takes an image, a model of the camera used to acquire that image, the pose of the camera during acquisition (as provided by an INS), and a DEM, and outputs an image that has been geo-rectified. The world coordinate of the bounds of the image are provided for viewing purposes. The code finds a mapping from points on the ground (DEM) to pixels in the image. By performing this process for all points on the ground, one can "paint" the ground with the image, effectively performing a projection of the image onto the ground. In order to make this process efficient, a method was developed for finding a region of interest (ROI) on the ground to where the image will project. This code is useful in any scenario involving an aerial imaging platform that moves and rotates over time. Many other applications are possible in processing aerial and satellite imagery.

Ansar, Adnan I.↗
NASA NTRS2009

Processing Images of Craters for Spacecraft Navigation

A crater-detection algorithm has been conceived to enable automation of what, heretofore, have been manual processes for utilizing images of craters on a celestial body as landmarks for navigating a spacecraft flying near or landing on that body. The images are acquired by an electronic camera aboard the spacecraft, then digitized, then processed by the algorithm, which consists mainly of the following steps: 1. Edges in an image detected and placed in a database. 2. Crater rim edges are selected from the edge database. 3. Edges that belong to the same crater are grouped together. 4. An ellipse is fitted to each group of crater edges. 5. Ellipses are refined directly in the image domain to reduce errors introduced in the detection of edges and fitting of ellipses. 6. The quality of each detected crater is evaluated. It is planned to utilize this algorithm as the basis of a computer program for automated, real-time, onboard processing of crater-image data. Experimental studies have led to the conclusion that this algorithm is capable of a detection rate >93 percent, a false-alarm rate <5 percent, a geometric error <0.5 pixel, and a position error <0.3 pixel.

Cheng, Yang↗
NASA NTRS2011

Daytime Water Detection Based on Sky Reflections

Robust water detection is a critical perception requirement for unmanned ground vehicle (UGV) autonomous navigation. This is particularly true in wide-open areas where water can collect in naturally occurring terrain depressions during periods of heavy precipitation and form large water bodies. One of the properties of water useful for detecting it is that its surface acts as a horizontal mirror at large incidence angles. Water bodies can be indirectly detected by detecting reflections of the sky below the horizon in color imagery. The Jet Propulsion Laboratory (JPL) has implemented a water detector based on sky reflections that geometrically locates the pixel in the sky that is reflecting on a candidate water pixel on the ground and predicts if the ground pixel is water based on color similarity and local terrain features. This software detects water bodies in wide-open areas on cross-country terrain at mid- to far-range using imagery acquired from a forward-looking stereo pair of color cameras mounted on a terrestrial UGV. In three test sequences approaching a pond under a clear, overcast, and cloudy sky, the true positive detection rate was 100% when the UGV was beyond 7 meters of the water's leading edge and the largest false positive detection rate was 0.58%. The sky reflection based water detector has been integrated on an experimental unmanned vehicle and field tested at Ft. Indiantown Gap, PA, USA.

sky reflections↗
NASA NTRS2011

Imaging Asteroid 4 Vesta Using the Framing Camera

The Framing Camera (FC) onboard the Dawn spacecraft serves a dual purpose. Next to its central role as a prime science instrument it is also used for the complex navigation of the ion drive spacecraft. The CCD detector with 1024 by 1024 pixels provides the stability for a multiyear mission and its high requirements of photometric accuracy over the wavelength band from 400 to 1000 nm covered by 7 band-pass filters. Vesta will be observed from 3 orbit stages with image scales of 227, 63, and 17 m/px, respectively. The mapping of Vesta s surface with medium resolution will be only completed during the exit phase when the north pole will be illuminated. A detailed pointing strategy will cover the surface at least twice at similar phase angles to provide stereo views for reconstruction of the topography. During approach the phase function of Vesta was determined over a range of angles not accessible from earth. This is the first step in deriving the photometric function of the surface. Combining the topography based on stereo tie points with the photometry in an iterative procedure will disclose details of the surface morphology at considerably smaller scales than the pixel scale. The 7 color filters are well positioned to provide information on the spectral slope in the visible, the depth of the strong pyroxene absorption band, and their variability over the surface. Cross calibration with the VIR spectrometer that extends into the near IR will provide detailed maps of Vesta s surface mineralogy and physical properties. Georeferencing all these observation will result in a coherent and unique data set. During Dawn s approach and capture FC has already demonstrated its performance. The strong variation observed by the Hubble Space Telescope can now be correlated with surface units and features. We will report on results obtained from images taken during survey mode covering the whole illuminated surface. Vesta is a planet-like differentiated body, but its surface gravity and escape velocity are comparable to those of other asteroids and hence much smaller than those of the inner planets or

Keller, H. Uwe↗
NASA NTRS2011

POSE Algorithms for Automated Docking

POSE (relative position and attitude) can be computed in many different ways. Given a sensor that measures bearing to a finite number of spots corresponding to known features (such as a target) of a spacecraft, a number of different algorithms can be used to compute the POSE. NASA has sponsored the development of a flash LIDAR proximity sensor called the Vision Navigation Sensor (VNS) for use by the Orion capsule in future docking missions. This sensor generates data that can be used by a variety of algorithms to compute POSE solutions inside of 15 meters, including at the critical docking range of approximately 1-2 meters. Previously NASA participated in a DARPA program called Orbital Express that achieved the first automated docking for the American space program. During this mission a large set of high quality mated sensor data was obtained at what is essentially the docking distance. This data set is perhaps the most accurate truth data in existence for docking proximity sensors in orbit. In this paper, the flight data from Orbital Express is used to test POSE algorithms at 1.22 meters range. Two different POSE algorithms are tested for two different Fields-of-View (FOVs) and two different pixel noise levels. The results of the analysis are used to predict future performance of the POSE algorithms with VNS data.

Heaton, Andrew F.↗
NASA NTRS2002

Wire Detection Algorithms for Navigation

In this research we addressed the problem of obstacle detection for low altitude rotorcraft flight. In particular, the problem of detecting thin wires in the presence of image clutter and noise was studied. Wires present a serious hazard to rotorcrafts. Since they are very thin, their detection early enough so that the pilot has enough time to take evasive action is difficult, as their images can be less than one or two pixels wide. Two approaches were explored for this purpose. The first approach involved a technique for sub-pixel edge detection and subsequent post processing, in order to reduce the false alarms. After reviewing the line detection literature, an algorithm for sub-pixel edge detection proposed by Steger was identified as having good potential to solve the considered task. The algorithm was tested using a set of images synthetically generated by combining real outdoor images with computer generated wire images. The performance of the algorithm was evaluated both, at the pixel and the wire levels. It was observed that the algorithm performs well, provided that the wires are not too thin (or distant) and that some post processing is performed to remove false alarms due to clutter. The second approach involved the use of an example-based learning scheme namely, Support Vector Machines. The purpose of this approach was to explore the feasibility of an example-based learning based approach for the task of detecting wires from their images. Support Vector Machines (SVMs) have emerged as a promising pattern classification tool and have been used in various applications. It was found that this approach is not suitable for very thin wires and of course, not suitable at all for sub-pixel thick wires. High dimensionality of the data as such does not present a major problem for SVMs. However it is desirable to have a large number of training examples especially for high dimensional data. The main difficulty in using SVMs (or any other example-based learning method) is the need for a very good set of positive and negative examples since the performance depends on the quality of the training set.

Kasturi, Rangachar↗