Tracking and data association
Robust Clustering with Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and robust techniques for detecting straight line segments using local models
Pattern Recognition Letters
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Navigating Mobile Robots: Systems and Techniques
Navigating Mobile Robots: Systems and Techniques
Journal of Intelligent and Robotic Systems
A Split-and-Merge Segmentation Algorithm for Line Extraction in 2-D Range Images
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Natural landmark extraction for mobile robot navigation based on an adaptive curvature estimation
Robotics and Autonomous Systems
Journal of Intelligent and Robotic Systems
Multi-sensor fusion for reduced uncertainty in autonomous mobile robot docking and recharging
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Automation of industrial vehicles: a vision-based line tracking application
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
Clustering and line detection in laser range measurements
Robotics and Autonomous Systems
Robust local localization of a mobile robot in indoor environments using virtual corners
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
EKF-Based Localization of a Wheeled Mobile Robot in Structured Environments
Journal of Intelligent and Robotic Systems
Robotics and Autonomous Systems
Prediction-based geometric feature extraction for 2D laser scanner
Robotics and Autonomous Systems
Improving area center robot navigation using a novel range scan segmentation method
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
A probabilistic approach to build 2d line based maps from laser scans in indoor environments
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Robotics and Autonomous Systems
Robotics and Autonomous Systems
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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.