DEM Quality Evaluation Framework
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Chris Gnam↗
Engineering topics
Chris Gnam
Publications and source records attributed to Chris Gnam.
A Comparison of Bearing Measurements to Surface Features Generated Using Stereophotoclinometry and Surface Feature Navigation Techniques
The Origins Spectral Interpretation Resource Identification Security Regolith Explorer (OSIRIS-REx) mission to the asteroid Bennu completed successful two-and-a-half year proximity operations in May 2021. The mission comprehensively mapped Bennu at unprecedented detail and collected a sample of Bennu’s surface to return to Earth. Throughout proximity operations, the OSIRIS-REx navigation team used the maps made of Bennu’s surface to navigate in the Bennu environment with high accuracy through the use of precise and accurate optical navigation data, radiometric data, and force modelling. The primary type of optical navigation measurements extracted from the images captured by OSIRIS-REx (particularly after first entering orbit around Bennu) were observations of known features on Bennu’s surface. Two related but different techniques/tools were used to extract these observations from the images: the Goddard Image Analysis and Navigation Tool Surface Feature Navigation (GIANT SFN) and Stereophotoclinometry (SPC) Autoregister. In this paper we compare the differences between the observables extracted using GIANT SFN and SPC Autoregister, explain the differences, and discuss where each technique is best suited.
Andrew Liounis↗
Digital Elevation Map Parametric Error Analysis Pipeline using Corresponding NAC Images
Future lunar landing systems, particularly those used to land humans on the lunar surface as part of the ARTEMIS program, will require precision navigation relative to the lunar surface. The most common way to meet these stringent navigation requirements is through terrain relative navigation (TRN), which localizes a spacecraft by comparing descent imagery with a predefined map of the surface. The accuracy achievable using TRN is limited by the accuracy of the reference Digital Elevation Map (DEM). It is therefore critical for future lunar missions that potential errors in DEMs be quantified. This paper describes one of NASA’s current efforts to develop a process for evaluating lunar DEM quality.
Chris Gnam↗
LuNaMaps FY 2024 Annual Program Review
The LuNaMaps project seeks to advance mapping capabilities and understanding in preparation for lunar landing scenarios. In this presentation we outline the advancements made by the team over the last year.
stereophotogrammetry↗
Technology Transfer Plan: LuNaMaps Project
The main contribution of this project is the combined knowledge of terrain relative navigation experts and lunar scientists who are familiar with both the lunar orbital imagery and the instruments that collected the data as well as how a TRN system utilizes map data. This knowledge comes in the form of published technical papers, benchmark map data sets, and software tools that can help others automate the process of creating the necessary maps for their own landing sites in the future. This document represents the project's plans to share all the lessons learned, processes developed, and applicable software tools with the public.
optical navigation↗
Tutorial on LuNaMaps Developed Tools andProcesses for Mapping the Lunar Surface
The main contribution of this project is the combined knowledge of terrain relative navigation experts and lunar scientists who are familiar with both the lunar orbital imagery and the instruments that collected the data as well as how a TRN system utilizes map data. This knowledge comes in the form of published technical papers, benchmark map data sets, and software tools that can help others automate the process of creating the necessary maps for their own landing sites in the future. This presentation provides a brief overview of the tools and processes developed by the project.
optical navigation↗
Celestial Navigation in Cislunar Space with autoNGC
Celestial navigation (CelNav) is a source of navigation observables where images of known solar system bodies are used to locate a spacecraft, beneficial within the solar system for both cislunar and deep space missions. CelNav provides a variety of design benefits to support and enable current and new autonomous space operations- using only a camera and a processor to produce in-situ measurements for navigation. This technology reduces subscription to ground-based tracking during all phases of a mission, freeing up resources for other operational needs. This also supports secure navigation since it eliminates the need for ground contact. CelNav enables missions where the light time delay between Earth and the spacecraft is too long (or the Earth to spacecraft line of sight is obscured) to support critical operations. It also enables smaller mission classes, where Deep Space Network (DSN)time is cost prohibitive, to reduce its cost by focusing primarily on data downlink. Finally, it enables the NASA Artemis program and other cislunar human space flight by providing redundant navigation to traditional radiometric tracking. In this presentation, we discuss the implementation of a CelNav app in autonomous Navigation, Guidance, and Control (autoNGC), a comprehensive flight software suite for onboard autonomy that is built on the core Flight System (cFS). The presentation also summarizes the results of flight software-in-the-loop (SIL) and processor-in-the-loop (PIL) demonstrations. Both are high-fidelity simulations with the use of a camera emulator hosted on a GPU server that simulates images that would be captured by the camera. The CelNav app leverages the use of cGIANT (cFS Goddard Image Analysis and Navigation Tool).Previously developed for the autoNGC software suite, cGIANT is an onboard autonomous image processing and optical navigation (OpNav) tool that performs limb-based OpNav and Terrain Relative Navigation. The added CelNav capability of cGIANT generates bearing measurements to multiple known celestial bodies (planets, moons, asteroids, comets, etc.) in monocular (2D) images. These observables are then fed to the Goddard Enhanced Onboard Navigation System (GEONS)navigation filter app, enabling us to navigate the spacecraft autonomously. In early 2025, the autoNGC CelNav capability is planned to be flight tested as part of the onboard autonomy experiment on the Cislunar Autonomous Positioning System Technology Operations and Navigation Experiment(CAPSTONE) spacecraft that is currently in a Lunar Near Rectilinear Halo Orbit(NRHO).
celestial navigation↗
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↗
Terrestrial Demonstration of Orbital Mapping and Validation Capabilities Over a Lunar Surface Analog
The Lunar Navigation Maps (LuNaMaps) project has improved existing tools and processes and developed new tools and processes to support the generation and validation of navigation maps of the lunar surface from orbital imagery. To demonstrate the advancements made through the LuNaMaps project, we conducted a terrestrial demonstration obtaining “orbital” imagery of the Lunar Surface Proving Ground (LSPG) at Astrobotic’s test facility in Mojave California. The LSPG is a 100 m by 100 m pad built to mimic the features and appearance of the lunar surface. In this paper we describe the planning and results of the test, including the capture of imagery for building the maps, the map building processes, building of a “truth map” using traditional surveying tools, and the map validation processes. We demonstrate how the built map compares to the “truth map” and how the validation processes provided insight to this comparison. Additionally, we describe an upcoming partner test in which the navigation maps will be used in a terrain relative navigation (TRN) technology demonstration over the same LSPG surface.
mapping↗