Detecting free space and obstacles in omnidirectional images

Abstract

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. © 2011 Springer-Verlag.