Authors:
Ales Jelinek
and
Ludek Zalud
Affiliation:
Brno University of Technology, Czech Republic
Keyword(s):
Vectorization, Point Cloud, Linear Regression, Least Squares Fitting, Mobile Robotics.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Optimization Problems in Signal Processing
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
Vision, Recognition and Reconstruction
Abstract:
Vectorization is a widely used technique in many areas, mainly in robotics and image processing. Applications
in these domains frequently require both speed (for real-time operation) and accuracy (for maximal
information gain). This paper proposes an optimization for the high speed vectorization methods, which leads
to nearly optimal results. The FTLS algorithm uses the total least squares method for fitting the lines into the
point cloud and the presented augmentation for the refinement of the results, is based on a modified NelderMead
method. As shown on several experiments, this approach leads to better utilization of the information
contained in the point cloud. As a result, the quality of approximation grows steadily with the number of
points being vectorized, which was not achieved before. Performance costs are still comparable to the original
algorithm, so the real-time operation is not endangered.