Commercial very high-resolution (VHR) Earth observing (EO) satellites have grown into constellations with global repeat coverage that can support existing NASA EO missions with stereo and multispectral capabilities. Sub-meter data from these instruments exceeds petabytes per year and the cost for data, storage systems and compute power have all dropped exponentially. Concurrently, through agreements with the National Geospatial-Intelligence Agency, NASA-Goddard Space Flight Center is acquiring VHR EO imagery from DigitalGlobe's WorldView-1, 2, 3 Quickbird-2, GeoEye-1 and IKONOS-2 satellites. To enhance the utility of these data we are developing an Application Program Interface (API) to produce on-demand user defined science ready products to support NASA's EO missions. These enhancements include two primary foci: 1) surface reflectance 1/2° ortho mosaics - multi-temporal 2 m multispectral imagery that can be used to investigate biodiversity, horizontal forest structure, surface water fraction, and land-cover land-use at the human scale; and 2) VHR digital elevation models (DEMs) - derived with the NASA Ames Stereo Pipeline. These enhanced products benefit Earth surface studies on the cryosphere (glacier mass balance, flow rates and snow depth), hydrology (lake/waterbody levels, landslides, subsidence) and the biosphere (vertical forest structure, tree canopy height and cover) among others. Here we present current API capabilities and recent examples of derived products used in NASA Earth Science projects.

We present an illumination-robust visual localization algorithm for Astrobee, a free-flying robot designed to autonomously navigate on the International Space Station (ISS). Astrobee localizes with a monocular camera and a pre-built sparse map composed of natural visual features. Astrobee must perform tasks not only during the day, but also at night when the ISS lights are dimmed. However, the localization performance degrades when the observed lighting conditions differ from the conditions when the sparse map was built. We investigate and quantify the effect of lighting variations on visual feature-based localization systems, and discover that maps built in darker conditions can also be effective in bright conditions, but the reverse is not true. We extend Astrobee's localization algorithm to make it more robust to changing-light environments on the ISS by automatically recognizing the current illumination level, and selecting an appropriate map and camera exposure time. We extensively evaluate the proposed algorithm through experiments on Astrobee.

We present the localization approach for Astrobee, a new free-flying robot designed to navigate autonomously on the International Space Station (ISS). Astrobee will accommodate a variety of payloads and enable guest scientists to run experiments in zero-g, as well as assist astronauts and ground controllers. Astrobee will replace the SPHERES robots which currently operate on the ISS, whose use of fixed ultrasonic beacons for localization limits them to work in a 2 meter cube. Astrobee localizes with monocular vision and an IMU, without any environmental modifications. Visual features detected on a pre-built map, optical flow information, and IMU readings are all integrated into an extended Kalman filter (EKF) to estimate the robot pose. We introduce several modifications to the filter to make it more robust to noise, and extensively evaluate the localization algorithm.

Accurate photometric reconstruction of the Lunar surface is important in the context of upcoming NASA robotic missions to the Moon and in giving a more accurate understanding of the Lunar soil composition. This paper describes a novel approach for joint estimation of Lunar albedo, camera exposure time, and photometric parameters that utilizes an accurate Lunar-Lambertian reflectance model and previously derived Lunar topography of the area visualized during the Apollo missions. The method introduced here is used in creating the largest Lunar albedo map (16% of the Lunar surface) at the resolution of 10 meters/pixel.