Title
Line Extraction in 2D Range Images for Mobile Robotics
Abstract
This paper presents a geometrical feature detection framework for use with conventional 2D laser rangefinders. This framework is composed of three main procedures: data pre-processing, breakpoint detection and line extraction. In data pre-processing, low-level data organization and processing are discussed, with emphasis to sensor bias compensation. Breakpoint detection allows to determine sequences of measurements which are not interrupted by scanning surface changing. Two breakpoint detectors are investigated, one based on adaptive thresholding, and the other on Kalman filtering. Implementation and tuning of both detectors are also investigated. Line extraction is performed to each continuous scan sequence in a range image by applying line kernels. We have investigated two classic kernels, commonly used in mobile robots, and our Split-and-Merge Fuzzy (SMF) line extractor. SMF employs fuzzy clustering in a split-and-merge framework without the need to guess the number of clusters. Qualitative and quantitative comparisons using simulated and real images illustrate the main characteristics of the framework when using different methods for breakpoint and line detection. These comparisons illustrate the characteristics of each estimator, which can be exploited according to the platform computing power and the application accuracy requirements.
Year
DOI
Venue
2004
10.1023/B:JINT.0000038945.55712.65
Journal of Intelligent and Robotic Systems
Keywords
Field
DocType
line extraction,breakpoint detection,2D range images,local environments,environment modeling,mobile robotics,Fuzzy C-means,linear Kalman filter
Computer vision,Fuzzy clustering,Fuzzy logic,Kalman filter,Artificial intelligence,Real image,Engineering,Thresholding,Detector,Mobile robot,Estimator
Journal
Volume
Issue
ISSN
40
3
1573-0409
Citations 
PageRank 
References 
104
6.42
16
Authors
2
Search Limit
100104
Name
Order
Citations
PageRank
Geovany Araujo Borges115412.82
Marie-José Aldon214711.61