<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this paper, a project and implementation of the parallel RANSAC algorithm in CUDA architecture for point cloud registration are presented. At the beginning, a serial state of the art method with several heuristic improvements from the literature compared to basic RANSAC is introduced. Subsequently, its algorithmic parallelization and CUDA implementation details are discussed. The comparative test has proven a significant program execution acceleration. The result is finding of the local coordinate system of the object in the scene in the near real-time conditions. The source code is shared on the Internet as a part of the Heuros system.</p></abstract>

In this paper, a project and implementation of the parallel RANSAC algorithm in CUDA architecture for point cloud registration are presented. At the beginning, a serial state of the art method with several heuristic improvements from the literature compared to basic RANSAC is introduced. Subsequently, its algorithmic parallelization and CUDA implementation details are discussed. The comparative test has proven a significant program execution acceleration. The result is finding of the local coordinate system of the object in the scene in the near real-time conditions. The source code is shared on the Internet as a part of the Heuros system.

Parallel RANSAC for Point Cloud Registration

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Parallel RANSAC for Point Cloud Registration"}

In this paper, a project and implementation of the parallel RANSAC algorithm in CUDA architecture for point cloud registration are presented. At the beginning,...

Parallel RANSAC for Point Cloud Registration

Published Online: Sep 09, 2017

Page range: 203 - 217

DOI: https://doi.org/10.1515/fcds-2017-0010

Keywords
RGBD, point clouds, registration, CUDA

© 2017 Daniel Koguciuk, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parallel RANSAC for Point Cloud Registration

Published Online: Sep 09, 2017

Page range: 203 - 217

DOI: https://doi.org/10.1515/fcds-2017-0010

KeywordsRGBD, point clouds, registration, CUDA

© 2017 Daniel Koguciuk, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
RGBD, point clouds, registration, CUDA