NDLI: Segmentation and parameterization of 2D lines based on Mean Shift Clustering

Content Provider	IEEE Xplore Digital Library
Author	Berrio, J.S. Paz, L.M. Bravo, E.C.
Copyright Year	2012
Description	Author affiliation: Robotics, Perception and Real Time Group (GRTR). Dept. Informática e Ingeniería de Sistemas (DIIS). Centro Politecnico Superior, University of Zaragoza, Spain (Paz, L.M.) \|\| Electrical and Electronic Engineering School at the Valle University, Cali, Colombia (Berrio, J.S.; Bravo, E.C.)
Abstract	This paper presents a robust algorithm for segmentation and characterization of lines detected by a laser sensor. We propose a strategy of Mean Shift Clustering which using the points of the laser scan performs a classification stage based on an ellipsoidal orientable window previous to the line segment parameterization. Each data set is processed by a RANSAC (Random Sample and Consensus) algorithm modified to detect spurious. This method reduces the amount of spurious and updates associated probability densities. The parameters of the detected segments are estimated by TLS (Total Least Squares) regression. The algorithm has been evaluated in indoor environments using as mobile platform the robot Pioneer 3DX equipped with a SICK laser.
Starting Page	1
Ending Page	6
File Size	727963
Page Count	6
File Format	PDF
ISBN	9781467308717
e-ISBN	9781467308700
DOI	10.1109/WEA.2012.6220085
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-02
Publisher Place	Colombia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mean shift Segmentation Lasers Clustering algorithms Laser Robot sensing systems Data models Vectors Kernel
Content Type	Text
Resource Type	Article

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