On the representation and estimation of spatial uncertainly
International Journal of Robotics Research
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Journal of Intelligent and Robotic Systems
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
Line Extraction in 2D Range Images for Mobile Robotics
Journal of Intelligent and Robotic Systems
Natural landmark extraction for mobile robot navigation based on an adaptive curvature estimation
Robotics and Autonomous Systems
An information-based exploration strategy for environment mapping with mobile robots
Robotics and Autonomous Systems
Clustering and line detection in laser range measurements
Robotics and Autonomous Systems
Prediction-based geometric feature extraction for 2D laser scanner
Robotics and Autonomous Systems
Robotics and Autonomous Systems
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In this paper a new technique is presented for online mapping of unknown indoor environments using laser range data scans performed by a mobile robot. The developed algorithm hierarchically utilizes clustering methods to convert data points into point-clusters and eventually to line-segments. In addition to using the K-means algorithm to form appropriate point-clusters, the Rank Order Clustering (ROC) technique is used for the first time in mapping, where no preset number of clusters is required for recognizing line clusters. To do this, a set of five fuzzy membership functions are designed for calculating the Similarity Index Matrix (SIM) of line-segments, after which line-segments lying in each cluster are merged to form the final perceived lines in the constructed map. The map-building process is performed dynamically: it incrementally adds new lines to the previously calculated map-lines and merges them with the overall map. Various simulations exhibit favorable results for a mobile robot navigating in indoor environments, both with static and dynamic obstacles.