International Journal of Computer Vision
The watershed transform: definitions, algorithms and parallelization strategies
Fundamenta Informaticae - Special issue on mathematical morphology
Fuzzy Logic Techniques for Autonomous Vehicle Navigation
Fuzzy Logic Techniques for Autonomous Vehicle Navigation
Appearance-Based Obstacle Detection with Monocular Color Vision
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Occupancy grids building by sonar and mobile robot
Robotics and Autonomous Systems
Learning valued preference structures for solving classification problems
Fuzzy Sets and Systems
Visual Navigation for Mobile Robots: A Survey
Journal of Intelligent and Robotic Systems
Region Classification for Robust Floor Detection in Indoor Environments
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Real-time map building and navigation for autonomous robots inunknown environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper introduces a new approach for detecting free space and obstacles in omnidirectional images that contributes to a purely vision based robot navigation in indoor environments. Naive Bayes classifiers fuse multiple visual cues and features generated from heterogeneous segmentation schemes that maintain separate appearance models and seeds for floor and obstacles regions. Pixel-wise classifications are aggregated across regions of homogeneous appearance to obtain a segmentation that is robust with respect to noise and outliers. The final classification utilizes fuzzy preference structures that interpret the individual classification as fuzzy preference relations which distinguish the uncertainty inherent to the classification in terms of conflict and ignorance. Ground truth data for training and testing the classifiers is obtained from the superposition of 3D scans captured by a photonic mixer device camera. The results demonstrate that the classification error is substantially reduced by rejecting those queries associated with a strong degree of conflict and ignorance.