Paper Info
Title
Using multicore processors to parallelize 3D point cloud registration with the Coarse Binary Cubes method
Abstract
This paper pursues speeding up 3D point cloud matching, which is crucial for mobile robotics. In previous work, we devised the Coarse Binary Cubes (CBC) method for fast and accurate registration of 3D scenes based on an integer objective function. Instead of point distance calculations, the method optimizes the number of coincident binary cubes between a pair of range images. In this paper, we propose taking advantage of widespread multicore and multithreaded processors to further speed-up CBC by parallel evaluation of prospective solutions in a globalized Nelder-Mead search. A performance analysis on two types of multicore processors is offered for indoor and outdoor scans from a 3D laser rangefinder. The proposed solution achieves a computational time gain close to the number of physical cores.
Year
DOI
Venue
2013
10.1109/ICMECH.2013.6518558
Mechatronics
Keywords
Field
DocType
computational complexity,control engineering computing,laser ranging,mobile robots,multi-threading,multiprocessing systems,search problems,3d laser rangefinder,3d point cloud registration,3d scenes,cbc method,coarse 3d point cloud matching,coarse binary cubes method,coincident binary cubes,computational time,globalized nelder-mead search,indoor scans,integer objective function,mobile robotics,multicore processors,multithreaded processors,outdoor scans,parallel evaluation,performance analysis,physical cores,multi threading
Multithreading,Computer science,Parallel computing,Point cloud,Multi-core processor,Mobile robot,Coincident,Cube,Computational complexity theory,Binary number
Conference
ISBN
Citations 
PageRank 
978-1-4673-1387-2
3
0.39
References 
Authors
15
5
Name
Order
Citations
PageRank
Jorge Martínez19517.02
Antonio J. Reina2354.47
Jesús Morales317916.38
Anthony Mandow414813.28
a garciacerezo550.77