Kevin Kobylka | Search NASA

NASA NTRSDate not supplied

Standalone Hazard Evaluation and Refinement From Instrument Findings (S.H.E.R.I.F.)

The Standalone Hazard Evaluation From Instrument Findings or SHERIF is a set of novel algorithms and associated framework designed to support the generation of Digital Elevation Maps (DEMs) from multiple LiDAR scans and perform Hazard Detection (HD) and Safe Site Selection with minimal input from other onboard systems. SHERIF can employ several techniques to perform Point Cloud registration(PCR) on disparate LiDAR scans of a planetary surface to generate a DEM which evolves over the course of a trajectory, with a flow designed to enable robustness. The framework also supports a variety of Hazard Detection and Safe Site Selection algorithms which can be run on the evolving DEM being produced. SHERIF features a robust and modular construction, allowing the user a high degree of flexibility in selecting and implementing whichever PCR and HD/SSL algorithms they may prefer, while maintaining the data products and sensor independence of the core SHERIF framework. SHERIF was recently tested in a hardware in the loop simulation at NASA JSC.

Entry Descent and Landing Guidance Navigation Cont↗

NASA NTRSDate not supplied

Camera Exposure Time Determination for Artemis I Lunar Flyby

During Artemis I, a flight test was conducted using the Optical Navigation Camera to image Lunar terrain at low altitude prior to the Return Powered Flyby (RPF) burn. The vehicle descended rapidly toward the surface and transitioned over the Lunar Terminator during the time frame in which the images were to be gathered, creating challenging lighting conditions which required the development of a novel technique for exposure time determination to gather imagery of appropriate quality for post flight analysis. The resulting technique leveraged simple photometric models and simulations, as well as the spacecrafts altitude and pointing direction to determine the necessary change in exposure times over the course of the flyby, resulting in a successful flight test.

Terrain Relative Navigation↗

NASA NTRSDate not supplied

Advancement of Deep Learning and Geometric Methods for Active Terrain Relative Navigation

To enhance NASA’s precision landing capabilities, in conjunction with the development of a novel active terrain relative navigation (ATRN) and terrain mapping system, denoted SHERIF, this work performed a comparative analysis between both deep-learning (DL) based and geometric approaches to hazard detection (HD) and safe-site-identification (SSI) through hardware-in-the loop testing on the Six degree-of-freedom Tendon Actuated Robot (STAR). The Standalone Hazard Evaluation and Refinement using Instrument Findings (SHERIF) system is capable of ingesting sensor data at an asynchronous rate, stitching successive terrain scans together to yield a high-resolution digital elevation map (DEM), performing absolute and relative localization using novel 3D feature extraction and matching methods, and HD/SSI activities. The DL-based HD/SSI algorithm provides a modular alternative to classical geometric approaches which have performance times that scale with map resolution. As the adoption of AI solutions become more prevalent for autonomous system decision making, it is prudent to explore the utility of such solutions in applications where they traditionally excel, such as image classification. Along with the development of a DL-based HD system, this work performed the first comparative analysis between DL and geometric approaches to HD/SSI using real sensor data from real-time testing in a relevant environment.

Davis Adams↗

NASA NTRSDate not supplied

Standalone Hazard Evaluation and Refinement From Instrument Findings (S.H.E.R.I.F.)

The Standalone Hazard Evaluation From Instrument Findings (SHERIF) system is a set of novel algorithms and associated framework designed to support the generation of Digital Elevation Maps (DEMs) from multiple LiDAR scans and perform Hazard Detection (HD) and Safe Site Identification (SSI) with no dependencies on other onboard systems. SHERIF can employ several techniques to perform robust 3D keypoint extraction and Point Cloud registration (PCR) on disparate LiDAR scans of a planetary surface to generate a DEM which evolves over the course of a trajectory. The framework also supports a variety of Hazard Detection and Safe Site Identification algorithms which can be applied to the evolving DEM being produced. SHERIF features a robust and modular construction, allowing the user a high degree of flexibility in selecting and implementing whichever keypoint identificaiton, PCR and HD/SSI algorithms they may prefer, while maintaining the data products and sensor independence of the core SHERIF framework. SHERIF was recently evaluated via simulation and hardware-in-the-loop experimental testing at NASA Johnson Space Center.

hazard detection↗

NASA NTRSDate not supplied

Standalone Hazard Evaluation and Refinement From Instrument Findings (S.H.E.R.I.F.)

The Standalone Hazard Evaluation From Instrument Findings (SHERIF) system is a set of novel algorithms and associated framework designed to support the generation of Digital Elevation Maps (DEMs) from multiple LiDAR scans and perform Hazard Detection (HD) and Safe Site Identification (SSI) with no dependencies on other onboard systems. SHERIF can employ several techniques to perform robust 3D keypoint extraction and Point Cloud registration (PCR) on disparate LiDAR scans of a planetary surface to generate a DEM which evolves over the course of a trajectory. The framework also supports a variety of Hazard Detection and Safe Site Identification algorithms which can be applied to the evolving DEM being produced. SHERIF features a robust and modular construction, allowing the user a high degree of flexibility in selecting and implementing whichever keypoint identificaiton, PCR and HD/SSI algorithms they may prefer, while maintaining the data products and sensor independence of the core SHERIF framework. SHERIF was recently evaluated via simulation and hardware-in-the-loop experimental testing at NASA Johnson Space Center.

hazard detection↗