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Predicting long-range traversability from short-range stereo-derived geometry
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Turmon, Michael Tang, Benyang Brjaracharya, Max Howard, Andrew |
| Copyright Year | 2010 |
| Description | Based only on its appearance in imagery, this program uses close-range 3D terrain analysis to produce training data sufficient to estimate the traversability of terrain beyond 3D sensing range. This approach is called learning from stereo (LFS). In effect, the software transfers knowledge from middle distances, where 3D geometry provides training cues, into the far field where only appearance is available. This is a viable approach because the same obstacle classes, and sometimes the same obstacles, are typically present in the mid-field and the farfield. Learning thus extends the effective look-ahead distance of the sensors. |
| File Size | 98254 |
| Page Count | 2 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_20100039425 |
| Archival Resource Key | ark:/13960/t1tf4sd9r |
| Language | English |
| Publisher Date | 2010-11-01 |
| Access Restriction | Open |
| Subject Keyword | Terrain Analysis Image Processing Terrain Mars Planet Imaging Techniques Mars Surface Imagery Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Technical Report |
