The Parallel Iterative Closest Point Algorithm

  1. (PDF, 519 KB)
AuthorSearch for: ; Search for: ; Search for:
ConferenceProceedings of the Third International Conference on 3D Digital Imaging and Modeling (3DIM), May 28 - June 1, 2001., Québec City, Québec, Canada
AbstractThis paper describes a parallel implementation developed to improve the time performance of the Iterative Closest Point Algorithm. Within each iteration, the correspondence calculations are distributed among the processor resources.At the end of each iteration, the results of the correspondence determination are communicated back to a central processor and the current transformation is calculated. A number of additional techniques were developed that served to improve upon this basic scheme. Calculating the partial sums within each distributed resource made it unnecessary to transmit the correspondence values back to the central processor, which reduced the communication overhead, and improved time performance. Randomly distributing the points among the processor resources resulted in a better load balancing, which further improved time performance. We also found that thinning the image by randomly removing a certain percentage of the points did not improve the performance, when viewed as the progression of <em>mse</em> with time. The method was implemented and tested on a 22 node Beowulf class cluster. For a large image, linear performance improvements were obtained for up to 16 processors, while they held for up to 8 processors with a smaller image.
Publication date
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number44184
NPARC number8913267
Export citationExport as RIS
Report a correctionReport a correction
Record identifier69df0800-3552-4491-9fc8-5cceec080cec
Record created2009-04-22
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)