a b s t r a c tWe adapted the automated, open source NASA Ames Stereo Pipeline (ASP) to generate digital elevation models (DEMs) and orthoimages from very-high-resolution (VHR) commercial imagery of the Earth. These modifications include support for rigorous and rational polynomial coefficient (RPC) sensor models, sensor geometry correction, bundle adjustment, point cloud co-registration, and significant improvements to the ASP code base. We outline a processing workflow for $0.5 m ground sample distance (GSD) DigitalGlobe WorldView-1 and WorldView-2 along-track stereo image data, with an overview of ASP capabilities, an evaluation of ASP correlator options, benchmark test results, and two case studies of DEM accuracy. Output DEM products are posted at $2 m with direct geolocation accuracy of <5.0 m CE90/LE90. An automated iterative closest-point (ICP) co-registration tool reduces absolute vertical and horizontal error to <0.5 m where appropriate ground-control data are available, with observed standard deviation of $0.1-0.5 m for overlapping, co-registered DEMs (n = 14, 17). While ASP can be used to process individual stereo pairs on a local workstation, the methods presented here were developed for large-scale batch processing in a high-performance computing environment. We are leveraging these resources to produce dense time series and regional mosaics for the Earth's polar regions. Ó

The Lunar CRater Observations and Sensing Satellite (LCROSS) mission impacted a spent Centaur rocket stage into a permanently shadowed region near the lunar south pole. The Sheperding Spacecraft (SSC) separated \sim9 hours before impact and performed a small braking maneuver in order to observe the Centaur impact plume, looking for evidence of water and other volatiles, before impacting itself. This paper describes the registration of imagery of the LCROSS impact region from the mid- and near-infrared cameras onboard the SSC, as well as from the Goldstone radar. We compare the Centaur impact features, positively identified in the first two, and with a consistent feature in the third, which are interpreted as a 20 m diameter crater surrounded by a 160 m diameter ejecta region. The images are registered to Lunar Reconnaisance Orbiter (LRO) topographical data which allows determination of the impact location. This location is compared with the impact location derived from ground-based tracking and propagation of the spacecraft's trajectory and with locations derived from two hybrid imagery/trajectory methods. The four methods give a weighted average Centaur impact location of -84.6796\circ, -48.7093\circ, with a 1{\sigma} un- certainty of 115 m along latitude, and 44 m along longitude, just 146 m from the target impact site. Meanwhile, the trajectory-derived SSC impact location is -84.719\circ, -49.61\circ, with a 1{\sigma} uncertainty of 3 m along the Earth vector and 75 m orthogonal to that, 766 m from the target location and 2.803 km south-west of the Centaur impact. We also detail the Centaur impact angle and SSC instrument pointing errors. Six high-level LCROSS mission requirements are shown to be met by wide margins. We hope that these results facilitate further analyses of the LCROSS experiment data and follow-up observations of the impact region.Comment: Accepted for publication in Space Science Review. 24 pages, 9 figure

Generating accurate three dimensional planetary models is becoming increasingly important as NASA plans manned missions to return to the Moon in the next decade. This paper describes a 3D surface reconstruction system called the Ames Stereo Pipeline that is designed to produce such models automatically by processing orbital stereo imagery. We discuss two important core aspects of this system: (1) refinement of satellite station positions and pose estimates through least squares bundle adjustment; and (2) a stochastic plane fitting algorithm that generalizes the Lucas-Kanade method for optimal matching between stereo pair images.. These techniques allow us to automatically produce seamless, highly accurate digital elevation models from multiple stereo image pairs while significantly reducing the influence of image noise. Our technique is demonstrated on a set of 71 high resolution scanned images from the Apollo 15 mission.

Bundle adjustment (BA) is the problem of refining a visual reconstruction to produce better structure and viewing parameter estimates. This problem is often formulated as a nonlinear least squares problem, where data arises from interest point matching. Mismatched interest points cause serious problems in this approach, as a single mismatch will affect the entire reconstruction. In this paper, we propose a novel robust Student's t BA algorithm (RST-BA). We model reprojection errors using the heavy tailed Student's t-distribution, and use an implicit trust region method to compute the maximum a posteriori (MAP) estimate of the camera and viewing parameters in this model. The resulting algorithm exploits the sparse structure essential for reconstructing multi-image scenarios, has the same time complexity as standard L 2 bundle adjustment (L 2 -BA), and can be implemented with minimal changes to the standard least squares framework. We show that the RST-BA is more accurate than either L 2 -BA or L 2 -BA with a σ-edit rule for outlier removal for a range of simulated error generation scenarios. The new method has also been used to reconstruct lunar topography using data from the NASA Apollo 15 orbiter, and we present visual and quantitative comparisons of RST-BA and L 2 -BA methods on this application. In particular, using the RST-BA algorithm we were able to reconstruct a DEM from unprocessed data with many outliers and no ground control points, which was not possible with the L 2 -BA method.

ABSTRACT:The integrated photogrammetric mapping system flown on the last three Apollo lunar missions (15, 16, and 17) in the early 1970s incorporated a Metric (mapping) Camera, a high-resolution Panoramic Camera, and a star camera and laser altimeter to provide support data. In an ongoing collaboration, the U.S. Geological Survey's Astrogeology Science Center, the Intelligent Robotics Group of the NASA Ames Research Center, and Arizona State University are working to achieve the most complete cartographic development of Apollo mapping system data into versatile digital map products. These will enable a variety of scientific/engineering uses of the data including mission planning, geologic mapping, geophysical process modelling, slope dependent correction of spectral data, and change detection. Here we describe efforts to control the oblique images acquired from the Apollo 15 Metric Camera.