Model Classes, Approximation, and Metrics for Dynamic Processing of Urban Terrain Data
Abstract
Theory, algorithms, and software have been developed for the analysis and processing of point cloud sensor data for representation, analysis and visualization of complex urban terrain. These involve various parameterizations of terrain data based on implicit surface representations and adaptive multiscale methods that enable high resolution and enhance understanding of topology and geometric features. The wavelet and multi scale methods enable fast computation and allow for varying local resolution of the data depending on the local density of the point cloud. The implicit representations which are developed facilitate highly accurate approximation of signed distances to the sensed terrain surface. The level sets of the signed distance provide efficiently computed field of view from specified observation points. Collaboration among MURI focus groups has yielded hybrid methods incorporating the best features of both approaches. Simulation and field experiments have been conducted to test the MURI methodologies. These include problems of sensor assimilation for autonomous navigation of urban terrain, surveillance, secure route planning, line of sight, target acquisition and a host of related problems.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2013
- Accession Number
- ADA586168
Entities
People
- Andrew Kurdila
- Guergana Petrova
- Hongkai Zhao
- Richard G. Baraniuk
- Richard Tsai
- Robert Sharpley
- Ronald DeVore
- Sanjeev Kulkarni
- Stanley Osher
Organizations
- University of South Carolina