ICRA 2011 Paper Abstract


Paper TuA114.4

Posada, Luis Felipe (Technische Universität Dortmund), Narayanan, Krishna Kumar (Technische Universität Dortmund), Hoffmann, Frank (Technische Universität Dortmund), Bertram, Torsten (Technische Universität Dortmund)

Ensemble of Experts for Robust Floor-Obstacle Segmentation of Omnidirectional Images for Mobile Robot Visual Navigation

Scheduled for presentation during the Regular Sessions "Visual Navigation I" (TuA114), Tuesday, May 10, 2011, 09:05−09:20, Room 5J

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 30, 2020

Keywords Visual Navigation, Omnidirectional Vision, Computer Vision for Robotics and Automation


This paper presents a novel approach for floor obstacle segmentation in omnidirectional images which rests upon the fusion of multiple classification generated from heterogeneous segmentation schemes. The individual naive Bayes classifiers rely on different features and cues to determine a pixel’s class label. 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 classification is supported by edge detection which indicate the presence of obstacles and sonar range data. The complementary expert decisions are aggregated by stacked generalization, behavior knowledge space or voting combination. The combined floor classifier achieves a classification accuracy of up to 0:96 true positive rate with only 0:03 false positive rate. A robust robot navigation is accomplished by arbitration among a reactive obstacle avoidance and a corridor following behavior using the robots local free space as perception.



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