Tracking and data association
A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
Characterization and detection of noise in clustering
Pattern Recognition Letters
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Mobile Robot Localization and Map Building: A Multisensor Fusion Approach
Mobile Robot Localization and Map Building: A Multisensor Fusion Approach
Directed Sonar Sensing for Mobile Robot Navigation
Directed Sonar Sensing for Mobile Robot Navigation
Computer Vision
Journal of Intelligent and Robotic Systems
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
On-line segment-based map building via integration of fuzzy systems and clustering algorithms
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Sensor Fusion for SLAM Based on Information Theory
Journal of Intelligent and Robotic Systems
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Simultaneous Localization and Map building (SLAM) is referred to as the ability of an Autonomous Mobile Robot (AMR) to incrementally extract the surrounding features for estimating its pose in an unknown location and unknown environment. In this paper, we propose a new technique for extraction of significant map features from standard Polaroid sonar sensors to address the SLAM problem. The proposed algorithm explicitly initializes and tracks the line (or wall) features from a comparison between two overlapping sensor measurements buffers. The experimental studies on a Pioneer 2DX mobile robot equipped with sonar sensors suggest that SLAM problem can be solved by the proposed algorithm. The estimated trajectory of AMR from the standard model based on Extended Kalman Filter (EKF) localization for the same experiment is also provided for comparison.