Monte Carlo localization: efficient position estimation for mobile robots
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Proceedings of the conference on Visualization '01
Robot Navigation for Automatic Model Construction Using Safe Regions
ISER '00 Experimental Robotics VII
Learning Occupancy Grid Maps with Forward Sensor Models
Autonomous Robots
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Incremental feature-based mapping from sonar data using Gaussian mixture models
Proceedings of the 2011 ACM Symposium on Applied Computing
Holography map for home robot: an object-oriented approach
Intelligent Service Robotics
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The problem of representing the environment of a mobile robot has been studied intensively in the past. The predominant approaches for geometric representations are grid-based or line-based maps. In this paper, we consider sample-based maps which use the data points obtained by range scanners to represent the environment. The main advantage of this representation over the other techniques is that it is able to represent arbitrary structures and at the same time provide an arbitrary accuracy. However, range measurements come in large amounts and not every measurement necessarily contributes to the representation in the same way. We present a novel approach for calculating maximum-likelihood subsets of the data points by sub-sampling laser range data. In particular, our method applies a variant of the fuzzy k-means algorithm to find a map that maximizes the likelihood of the original data. Experimental results with real data show that the resulting maps are better suited for robot localization than maps obtained with other sub-sampling techniques.