Abstract
A method for automated registration of lidar datasets specifically tailored to geometries with high length-to-width ratios operates on data in curvilinear coordinates. It relaxes the minimum change in perspective requirement between neighboring datasets typical of other algorithms. Range data is filtered with a series of discrete Gaussian and derivative of Gaussian filters to form a second-order Taylor series approximation to the surface about each sampled point. Principal curvatures with respect to the surface normal are calculated and compared across neighboring datasets to determine homologies and the best fit transfer matrix. The method reduces raw data volume requirements and processing time.
Original language | English (US) |
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Title of host publication | Laser Radar Technology and Applications XXI |
Publisher | SPIE |
Volume | 9832 |
ISBN (Electronic) | 9781510600737 |
DOIs | |
State | Published - 2016 |
Event | Laser Radar Technology and Applications XXI - Baltimore, United States Duration: Apr 19 2016 → Apr 20 2016 |
Other
Other | Laser Radar Technology and Applications XXI |
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Country | United States |
City | Baltimore |
Period | 4/19/16 → 4/20/16 |
Keywords
- 3D registration
- invariant feature
- laser radar
- scale invariance
- spherical coordinate
- surface matching
- tunnel
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering