Characterization and detection of noise in clustering
Pattern Recognition Letters
The map-building and exploration strategies of a simple sonar-equipped mobile robot
The map-building and exploration strategies of a simple sonar-equipped mobile robot
A fuzzy approach to build sonar maps for mobile robots
Computers in Industry
Fuzzy Sets and Systems
Fuzzy Modeling for Control
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Multisensor Fusion: An Autonomous Mobile Robot
Journal of Intelligent and Robotic Systems
Incorporation of Feature Tracking into Simultaneous Localization and Map Building via Sonar Data
Journal of Intelligent and Robotic Systems
A Robust Regression Model for Simultaneous Localization and Mapping in Autonomous Mobile Robot
Journal of Intelligent and Robotic Systems
Sensor Fusion for SLAM Based on Information Theory
Journal of Intelligent and Robotic Systems
An Entropy Optimization Strategy for Simultaneous Localization and Mapping
Journal of Intelligent and Robotic Systems
Incremental feature-based mapping from sonar data using Gaussian mixture models
Proceedings of the 2011 ACM Symposium on Applied Computing
Robotics and Autonomous Systems
Robotics and Autonomous Systems
A learning based self-organized additive fuzzy clustering method and its application for EEG data
International Journal of Knowledge-based and Intelligent Engineering Systems - Intelligent Information Processing: Techniques and Applications
Hi-index | 0.00 |
In this paper, we present a technique for on-line segment-based map building in an unknown indoor environment from sonar sensor observations. The world model is represented with two-dimensional line segments. The information obtained by the ultrasonic sensors is updated instantaneously while the mobile robot is moving through the workspace. An Enhanced Adaptive Fuzzy Clustering Algorithm (EAFC) along with Noise Clustering (NC) is proposed to extract and classify the line segments in order to construct a complete map for an unknown environment. Furthermore, to alleviate the problem of extensive computation associated with the process of map building, the workplace of the mobile robot is divided into square cells. A compatible line segment merging technique is then suggested to combine the similar segments after the extraction of the line segment by EAFC along with NC algorithm. The performance of the algorithm is demonstrated by experimental results on a Pioneer II mobile robot.