Target recognition by components for mobile robot navigation

This paper presents a vision-based technique for detecting targets of the environment which have to be reached by an autonomous mobile robot during its navigational tasks. The targets the robot has to reach are the doors of the authors' office building. The detection of the door has been performed by detecting its most significant components in the image and it is based on data classification. Two neural classifiers have been trained for recognizing single components of the door. Then a combining algorithm, based on heuristic considerations, checks that they are in the proper geometric configuration of the structure of the door. The novelty of this work is to use together colour and shape information for identifying features and for detecting the components of the target. The approach, based on learning by components, is able to cleverly solve the problems of scale changes, perspective variations and partial occlusions. The obtained detecting system has been tested on a large test set of real images showing a high reliability and robustness: doors of different rooms, under different illumination conditions and by different viewpoints have been successfully recognized. Results in terms of door detection rate and false positive rate are presented throughout the paper.