Name
Abstract | ||
|---|---|---|
Robust estimation method is proposed to combine multiple observations and create consistent, accurate, dense Digital Elevation Models (DEMs) from lunar orbital imagery. The NASA Ames Intelligent Robotics Group (IRG) aims to produce higher-quality terrain reconstructions of the Moon from Apollo Metric Camera (AMC) data than is currently possible. In particular, IRG makes use of a stereo vision process, the Ames Stereo Pipeline (ASP), to automatically generate DEMs from consecutive AMC image pairs. However, the DEMs currently produced by the ASP often contain errors and inconsistencies due to image noise, shadows, etc. The proposed method addresses this problem by making use of multiple observations and by considering their goodness of fit to improve both the accuracy and robustness of the estimate. The stepwise regression method is applied to estimate the relaxed weight of each observation. |
Year | Venue | Keywords |
|---|---|---|
2010 | ISVC | robust estimation method,ames stereo pipeline,multiple observation,apollo metric camera,image noise,dense digital elevation models,robust mosaicking,consecutive amc image pair,stereo digital elevation model,stepwise regression method,nasa ames intelligent robotics,goodness of fit,estimating,algorithms,digital elevation models,robust estimator,cross correlation,terrain,stepwise regression,digital elevation model,shadows,stereo vision |
Field | DocType | Volume |
Computer vision,Pattern recognition,Bundle adjustment,Computer science,Stereopsis,Terrain,Stereophotography,Image noise,Robustness (computer science),Digital elevation model,Artificial intelligence,Robotics | Conference | 6454 |
ISSN | ISBN | Citations |
0302-9743 | 3-642-17273-3 | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taemn Kim | 1 | 382 | 28.18 |
| Zachary Moratto | 2 | 7 | 1.94 |
| Ara V. Nefian | 3 | 751 | 56.08 |