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NASA NTRS2017

A Consistent EPIC Visible Channel Calibration using VIIRS and MODIS as a Reference

The Earth Polychromatic Imaging Camera (EPIC) aboard the Deep Space Climate Observatory (DSCOVR) satellite constantly images the sunlit disk of Earth from the Lagrange-1 (L1) point in 10 spectral channels spanning the UV, VIS, and NIR spectrums. Recently, the DSCOVR EPIC team has publicly released version 2 dataset, which has implemented improved navigation, stray-light correction, and flat-fielding of the CCD array. The EPIC 2-year data record must be well-calibrated for consistent cloud, aerosol, trace gas, land use and other retrievals. Because EPIC lacks onboard calibrators, the observations made by EPIC channels must be calibrated vicariously using the coincident measurements from radiometrically stable instruments that have onboard calibration systems. MODIS and VIIRS are best-suited instruments for this task as they contain similar spectral bands that are well-calibrated onboard using solar diffusers and lunar tracking. We have previously calibrated the EPIC version 1 dataset by using EPIC and VIIRS angularly matched radiance pairs over both all-sky ocean and deep convective clouds (DCC). We noted that the EPIC image required navigations adjustments, and that the EPIC stray-light correction provided an offset term closer to zero based on the linear regression of the EPIC and VIIRS ray-matched radiance pairs. We will evaluate the EPIC version 2 navigation and stray-light improvements using the same techniques. In addition, we will monitor the EPIC channel calibration over the two years for any temporal degradation or anomalous behavior. These two calibration methods will be further validated using desert and DCC invariant Earth targets. The radiometric characterization of the selected invariant targets is performed using multiple years of MODIS and VIIRS measurements. Results of these studies will be shown at the conference.

Haney, C. O.↗

NASA NTRS2005

Single-Command Approach and Instrument Placement by a Robot on a Target

AUTOAPPROACH is a computer program that enables a mobile robot to approach a target autonomously, starting from a distance of as much as 10 m, in response to a single command. AUTOAPPROACH is used in conjunction with (1) software that analyzes images acquired by stereoscopic cameras aboard the robot and (2) navigation and path-planning software that utilizes odometer readings along with the output of the image-analysis software. Intended originally for application to an instrumented, wheeled robot (rover) in scientific exploration of Mars, AUTOAPPROACH could be adapted to terrestrial applications, notably including the robotic removal of land mines and other unexploded ordnance. A human operator generates the approach command by selecting the target in images acquired by the robot cameras. The approach path consists of multiple legs. Feature points are derived from images that contain the target and are thereafter tracked to correct odometric errors and iteratively refine estimates of the position and orientation of the robot relative to the target on successive legs. The approach is terminated when the robot attains the position and orientation required for placing a scientific instrument at the target. The workspace of the robot arm is then autonomously checked for self/terrain collisions prior to the deployment of the scientific instrument onto the target.

Huntsberger, Terrance↗

NASA NTRS2019

Thermal testing of a Mars 2020 enhanced engineering camera

The upcoming Mars 2020 rover mission is introducing a new generation of engineering cameras (ECAMs) called the enhanced engineering cameras (EECAMs), which have advanced imaging capability over the previous ECAMs used on the Spirit, Opportunity, and Curiosity rovers. The Mars 2020 rover will have nine EECAMs – six upgraded HazCams used for hazard avoidance, two upgraded NavCams used for navigation, and a single CacheCam used to take images of samples obtained by the rover’s Sampling and Caching Subsystem (SCS). The detailed EECAM design was completed in April 2017, and since then the EECAM Subsystem has been in the process of fabrication, integration, and testing. This paper describes two thermal tests that were done for thermal model correlation and validation of the EECAM thermal design during Mars surface operations.

Novak, Keith S.↗

NASA NTRSDate not supplied

The LuNaMaps Project: Advancing Capabilities for Developing and Validating Digital Elevation Models of Rocky Surfaces from Orbital Data

Both navigation and surface science can benefit from the ability to generate high resolution and accurate maps of the surface of the Moon and other solar system bodies. The primary way these maps are generated is through the use of orbital imagery and ranging data. Traditionally, the process of using orbital imagery and ranging data is tedious and labor-intensive. Additionally, once maps have been built, there has generally been limited effort in developing standards by which to verify the accuracy and quality of the generated maps. The Lunar Navigation Maps (LuNaMaps) project is a NASA Game Changing Development (GCD) project which over the last 4 years has aimed to address these issues both for the Moon and for other rocky solar system bodies. This has been accomplished through development of new and existing capabilities including: a suite of methods and tools to combine all sources of orbital imagery; a benchmark data set as well as basic requirements for high-fidelity simulations of precision landing functions; tools to synthetically enhance map products with lander-scale features for use in the development and testing of hazard detection systems; methods and tools to evaluate the accuracy of developed digital elevation maps (DEMs) and their quality for use in terrain relative navigation scenarios; and tools to realistically render image and lidar data. In this work, we provide an overview of the capabilities developed through LuNaMaps, demonstrating its use for processing existing lunar data, and describing how it can be applied to other use cases. We additionally provide preliminary results showing the application of the developed tools and processes to the generation of elevation maps of the Lunar Surface Proving Grounds (LSPG) lunar analog at Astrobotic’s Mojave testing facility using “orbital imagery” captured by a drone. In this terrestrial demonstration, we have the benefit of being able to compare the results to a ground truth model of the LSPG. We finally describe plans to use the newly created maps in a terrestrial terrain relative navigation demonstration over the LSPG in early 2025.

optical navigation↗

NASA NTRS1975

Demonstration of optical navigation measurements on Mariner 10

An experiment was performed on the Mariner 10 Venus/Mercury mission to assess the performance of the onboard television cameras and ground-based software used in the optical navigation measurement system. The elements of the system, calibration, and distortion are considered. The navigation technique requires detection of stars in the same field of view as the target body. The star detection capability is discussed with reference to required threshold, calibration factors, standard conditions, camera sensitivity, response uniformity, and the effect of image smearing. Tests conducted during the Mariner 10 encounters with Mercury demonstrated that a large bright planet can be imaged simultaneously with faint stars to provide accurate navigation data. Improvements are suggested in the system for the Mariner Jupiter/Saturn 1977 and later missions.

Stanton, R. H.↗

NASA NTRS2007

Mapped Landmark Algorithm for Precision Landing

A report discusses a computer vision algorithm for position estimation to enable precision landing during planetary descent. The Descent Image Motion Estimation System for the Mars Exploration Rovers has been used as a starting point for creating code for precision, terrain-relative navigation during planetary landing. The algorithm is designed to be general because it handles images taken at different scales and resolutions relative to the map, and can produce mapped landmark matches for any planetary terrain of sufficient texture. These matches provide a measurement of horizontal position relative to a known landing site specified on the surface map. Multiple mapped landmarks generated per image allow for automatic detection and elimination of bad matches. Attitude and position can be generated from each image; this image-based attitude measurement can be used by the onboard navigation filter to improve the attitude estimate, which will improve the position estimates. The algorithm uses normalized correlation of grayscale images, producing precise, sub-pixel images. The algorithm has been broken into two sub-algorithms: (1) FFT Map Matching (see figure), which matches a single large template by correlation in the frequency domain, and (2) Mapped Landmark Refinement, which matches many small templates by correlation in the spatial domain. Each relies on feature selection, the homography transform, and 3D image correlation. The algorithm is implemented in C++ and is rated at Technology Readiness Level (TRL) 4.

Johnson, Andrew↗

NASA NTRS2018

A Window in the Future of Planetary Surface Navigation

The presentation focuses on surface navigation and mapping challenges in planetary environments including Lunar and Martian surface. Imagery from precursor orbital missions are processed to provide a medium resolution, large coverage 2D and 3D maps used by the science and navigation teams. During the surface mission these mapping products together with the images captured from the on-board camera systems are used in rover localization and navigation.

planetary exploration↗

NASA NTRS2016

Challenges in Planetary Mapping and Surface Navigation

planetary mapping↗

NASA NTRS1990

Simple technique for optical flow estimation

The theoretical principles, design, and implementation of an image-motion detection system with possible robotics and autonomous-navigation applications are discussed. The operation of a simple one-dimensional second-derivative edge-motion detector is explained; its extension to a two-dimensional motion sensor based on the Laplacian of a Gaussian (Marr and Hildreth, 1980) is outlined; a center-surround array of 13 of these sensors (12 in a circle around a central sensor) is proposed; and processing techniques to overcome the aperture problem are examined. Simple video images involving unidirectional motion are then analyzed using a prototype version of this design; the sensors employed are based on a 15 x 15-pixel mask made up of 13 7 x 7-pixel kernels. Accurate measurements of image velocity are obtained, as required for navigation through complex environments. It is pointed out that the procedure is noniterative and well suited to implementation as a parallel network.

Perrone, John A.↗

NASA NTRS1990

Image based range determination

Flight vehicles operating at low altitudes such as rotorcraft require range determination schemes for locating the terrain and obstacles. The development of a ranging scheme combining image sequences from vehicle mounted passive imaging sensors and the vehicle motion variables obtained from an on-board inertial navigation system is described. This approach can handle sequences from more than one imaging device. Derivation of the numerical algorithm and the performance results using a laboratory image sequence are given. Other applications of the proposed approach include ranging schemes for autonomous planetary rovers and telerobots.

Menon, P. K. A.↗

NASA NTRSDate not supplied

Optical Navigation Attitude Estimation and Calibration Performance Improvement using Outlier Rejection

Spacecraft optical navigation (OpNav) systems process a sequence of images of celestial bodies against a starfield background to estimate the position and velocity of the vehicle. While attitude is sometimes available from an onboard star tracker, it is often desirable to recognize the background stars in the OpNav images to better align the image. While many image processing algorithms exist for finding stars, efficiency and reliability remain key issues in the presence of extended bodies(e.g. the Moon, Earth), especially when attempting to solve the full lost-in-space problem. Some star outliers(stars identified with high residuals)could appear in the camera field of view, however using them in the attitude estimation or camera calibration would lead to less accurate results. Therefore, we require new and robust approaches to remove these outliers before any further processing. The emphasis of the work is on developing a simple and robust iterative technique to detect and reject the outliers which could be found in any frame during the lost in space attitude determination or during the camera calibration. These outliers are determined based on the residuals of the centroids of the detected stars and the corresponding location using the star catalog. If the residuals exceed a predetermined threshold value, the object will be detected as an outlier and will be removed before another attitude determination and calibration iteration is performed. The performance for both attitude determination and on-orbit camera calibration are improved by an almost two-fold increase in accuracy when applying this outlier rejection technique.

OpNav↗

NASA NTRS2018

Spacecraft Alignment Determination and Control for Dual Spacecraft Precision Formation Flying

Many proposed formation flying missions seek to advance the state of the art in spacecraft science imaging by utilizing dual-spacecraft precision formation flying (PFF) to enable a 'virtual' telescope (VT). Using precision dual-spacecraft alignment, very long focal lengths can be achieved by locating the optics on one spacecraft and the detector on the other. Proposed science missions include astrophysics concepts for X-ray imaging and exo-planet observation with large spacecraft separations (1000 km to 80,000 km), and heliophysics concepts for X-ray or extreme ultra-violet (EUV) imaging or solar coronagraphs with smaller separations (50m - 500m). These proposed missions require advances in guidance, navigation, and control (GN&C) for PFF to enable high resolution science imaging. For many applications, the dual-spacecraft dynamics are coupled through the GN&C system when the relative ranging and position alignment sensor components are not co-located with their respective spacecraft mass centers. We develop a model-based PFF system design approach for the VT application, considering the coupling inherent in precision dual-spacecraft inertial alignment. These systems employ a variety of GN&C sensors and actuators, including laser-based alignment and ranging systems, camera-based imaging sensors, inertial measurement units (IMU), as well as microthruster systems and image motion compensation platforms. Results of a GN&C performance assessment reveal how data from relative position sensors can be employed in a Kalman filter framework to significantly improve alignment estimation performance. The assessment provides a comparison of two different GN&C formation flying architectures, illustrating the performance trades inherent in the choice of PFF system architecture in the VT application.

Formation Flying↗

NASA NTRS2008

ULTOR(Registered TradeMark) Passive Pose and Position Engine For Spacecraft Relative Navigation

The ULTOR(Registered TradeMark) Passive Pose and Position Engine (P3E) technology, developed by Advanced Optical Systems, Inc (AOS), uses real-time image correlation to provide relative position and pose data for spacecraft guidance, navigation, and control. Potential data sources include a wide variety of sensors, including visible and infrared cameras. ULTOR(Registered TradeMark) P3E has been demonstrated on a number of host processing platforms. NASA is integrating ULTOR(Registerd TradeMark) P3E into its Relative Navigation System (RNS), which is being developed for the upcoming Hubble Space Telescope (HST) Servicing Mission 4 (SM4). During SM4 ULTOR(Registered TradeMark) P3E will perform realtime pose and position measurements during both the approach and departure phases of the mission. This paper describes the RNS implementation of ULTOR(Registered TradeMark) P3E, and presents results from NASA's hardware-in-the-loop simulation testing against the HST mockup.

Hannah, S. Joel↗

NASA NTRS2006

Marsviewer

Marsviewer is a multi-platform application designed to aid in quality control, browsing, and analysis of original science product images (Experiment Data Records, or EDRs) and derived image data products (Reduced Data Records, or RDRs) returned by the Mars Explorer Rover (MER) mission. Marsviewer offers an abstraction of the products organization via a file finder. For example, the application understands the file structure and filename conventions of the MER Operational Storage Server, helping the user to navigate this complex file system to find desired images. Marsviewer also works with a flat file system, remote-operations file systems, image-archive file systems, and others. All EDRs found for a given solar day (Sol) are displayed in a list, optionally with thumbnail images. Once the user selects an image from the list, a tabbed pane conveniently displays the original source image and all associated RDRs. Marsviewer provides the option of overlaying derived images upon the source image, resulting in an easier-to-interpret color representation of the data. Display manipulations such as zoom, data range adjustment, contrast enhancement, and contour control are available. Image metadata (labels) from the current image can be displayed and searched. The architecture of the program is extensible: new types of RDRs can be installed and new file finders can be added to adapt the program to different file structures and different filename conventions. This keeps the application flexible and provides an opportunity for reuse with future rover missions.

Toole, Nicholas↗

NASA NTRS1973

Topographical coordinates from Mariner 9 observations of the Martian surface

Results of Mariner 9 determinations of the coordinates of selected topographical features on Mars. An attempt is made to project uncertainties in such determinations, together with uncertainties in spin-rate and in the inertial orientation of the spin axis of Mars, to some epoch in the distant future, and, to examine the impact of further imaging data from a Mars synchronous orbiter in reducing the uncertainty of the location of any given surface feature on the planet relative to the orbiter. The effect of residual navigational uncertainties, and uncertainties in celestial orientation of the imaging system, on such determinations is discussed.

Mohan, S. N.↗

NASA NTRS1993

Precise navigation for the Earth Observing System (EOS)-AM1 spacecraft using the TDRSS Onboard Navigation System (TONS)

As the baseline navigation system for the Earth Observing System (EOS)-AM1 spacecraft, the Tracking and Data Relay Satellite System (TDRSS) Onboard Navigation System (TONS) is required to provide precise position and velocity information for imaging instrument calibration and routine operations. This paper presents the results of real-time navigation performance evaluations with respect to TONS-based orbit and frequency determination to satisfy this requirement. Both covariance and simulation analysis of EOS-AM1 navigation accuracy and analysis using operational data from Landsat-4 are presented. Local (half orbit) and global (multiple orbits) tracking are considered using a way-forward link services. Improvements in navigation accuracies by using enhanced gravity models beyond the Goddard Earth Model (GEM)-T3 are also discussed. Key objectives of the analysis are to evaluate nominal performance and potential sensitivities and to address algorithm improvements such as TDRS ephemeris biasing, ionosphere model, and gravity process noise models slated for implementation. Results indicate that TONS can be configured to meet the proposed instrument navigation requirements of 20 meters, 3-sigma.

Folta, David C.↗

NASA NTRS1974

Mariner IX optical navigation using Mars lit limb

Optical spacecraft navigation data, i.e., the lit limb TV image of Mars, acquired during the approach phase of the Mariner IX spacecraft to Mars, has been successfully demonstrated to augment the radio spacecraft tracking data. Accurate in-flight calibration of the TV instrument and the scan platform was performed by referencing stars and planets. Simulated real-time processing and the detailed postflight analyses of the onboard optical data have shown that planet limb data is an important data source in a far-encounter period for which other types of onboard optical measurements, e.g., natural satellite(s) of the target planet with star background, may not be available.

Jerath, N.↗

NASA NTRS1987

NAECON 87; Proceedings of the IEEE National Aerospace and Electronics Conference, Dayton, OH, May 18-22, 1987. Volumes 1, 2, 3, & 4

The present conference discusses topics in VLSI components and their packaging, signal processing, uses of cartographic data, data transmission, advanced avionics architectures, fiber-optics, information control and display, image processing, airborne radar and fire control, navigation, air data, Kalman filtering, power generation and control, spacecraft power structures, aircraft flying qualities, flight management, fault-tolerant computer architectures, actuation technologies, self-repairing flight control system technology, multivariable control, stability and control methods, and AFTI/F-16 flight test reports. Also discussed are the ADA/JOVIAL language and its applications, software acquisition and testing, advanced software concepts, software management, computer graphics and visual systems softwear, ADA in embedded avionics, 16- and 32-bit architectures, voice interaction applications, human/machine systems analysis, human factors and AI, mental workloads and displays, pilot acceleration protection research, communications system technology, space communications, reliability and maintainability, managerial techniques, engineering management, EM compatibility and nuclear hardening, expert systems, AI language/knowledge representation, expert system implementation, machine vision/optical processing, and advanced AI concepts and architectures.

Avionics↗