Authors:
David Eriksson
and
Evan Shellshear
Affiliation:
Fraunhofer-Chalmers Centre, Sweden
Keyword(s):
Point Clouds, Distance Computation, Path-Planning, Simplification.
Related
Ontology
Subjects/Areas/Topics:
Computer-Based Manufacturing Technologies
;
Facilities Planning and Management
;
Industrial Automation and Robotics
;
Industrial Engineering
;
Industrial Networks and Automation
;
Informatics in Control, Automation and Robotics
;
Intelligent Design and Manufacturing
;
Robotics and Automation
;
Systems Modeling and Simulation
Abstract:
In this paper, algorithms have been developed that are capable of efficiently pre-processing massive point clouds for the rapid computation of the shortest distance between a point cloud and other objects (e.g. triangulated, point-based, etc.). This is achieved by exploiting fast distance computations between specially structured subsets of a simplified point cloud and the other object. This approach works for massive point clouds even with a small amount of RAM and was able to speed up the computations, on average, by almost two orders of magnitude. Given only 8 GB of RAM, this resulted in shortest distance computations of 30 frames per second for a point cloud originally having 1 billion points. The findings and implementations will have a direct impact for the many companies that want to perform path-planning applications through massive point clouds since the algorithms are able to produce real-time distance computations on a standard PC.