Adaptive Range Sampling Using a Stochastic Model

[+] Author and Article Information
Zvi Devir

Department of Computer Science, Technion-Israel Institute of Technology, Haifa 32000, Israelzdevir@cs.technion.ac.il

Michael Lindenbaum

Department of Computer Science, Technion-Israel Institute of Technology, Haifa 32000, Israelmic@cs.technion.ac.il

This estimator is the maximum likelihood estimator of the variance, and is biased. Since the bias is a constant factor, the priority function may use either this estimator or the unbiased estimator.

The weighting functions are invariant under translations of the coordinate system. A weighting function is invariant under scaling of the coordinate system, if scaling of the coordinate system does not change ratios between the weights. That is, w(x,y1)w(x,y2)=w(αx,αy1)w(αx,αy2), where x, y1, and y2 are points, and α>0 is the scaling factor of the coordinate system.

J. Comput. Inf. Sci. Eng 7(1), 20-25 (Dec 10, 2006) (6 pages) doi:10.1115/1.2432899 History: Received September 14, 2005; Revised December 10, 2006

We consider the task of sequential point sampling for three-dimensional structure reconstruction and focus on terrestrial topographic mapping using a laser range scanner. Both the sampling and the reconstruction rely on a stochastic model of the sampled object. We describe several algorithms for sequential point sampling including a new adaptive algorithm that is specifically designed for mechanical devices and produces grid-like sampling patterns. Experimental results verify that relying on the stochastic model indeed leads to efficient sampling associated with accurate surface reconstruction.

Copyright © 2007 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.



Grahic Jump Location
Figure 1

One iteration of the nonadaptive FPS algorithm: (a) the sampled points (sites) and the corresponding Voronoi diagram; (b) the candidates for sampling (Voronoi vertices); (c) the farthest candidate chosen for sampling; and (d) the updated Voronoi diagram

Grahic Jump Location
Figure 2

Sampling points produced by the AFPS algorithm. Each distribution is composed of 25 sampling points.

Grahic Jump Location
Figure 3

Synthetic DTM (1200×1200). The bold V shape marks the scanner’s point of view.

Grahic Jump Location
Figure 4

A range image (left) and its correlation function (right)

Grahic Jump Location
Figure 5

Sampling patterns with 561 points: (a) regular FPS; (b) restricted FPS; (c) adaptive FPS; and (d) adaptive grid



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In