Paper Info
Title
Object Detection Applied to Indoor Environments for Mobile Robot Navigation.
Abstract
To move around the environment, human beings depend on sight more than their other senses, because it provides information about the size, shape, color and position of an object. The increasing interest in building autonomous mobile systems makes the detection and recognition of objects in indoor environments a very important and challenging task. In this work, a vision system to detect objects considering usual human environments, able to work on a real mobile robot, is developed. In the proposed system, the classification method used is Support Vector Machine (SVM) and as input to this system, RGB and depth images are used. Different segmentation techniques have been applied to each kind of object. Similarly, two alternatives to extract features of the objects are explored, based on geometric shape descriptors and bag of words. The experimental results have demonstrated the usefulness of the system for the detection and location of the objects in indoor environments. Furthermore, through the comparison of two proposed methods for extracting features, it has been determined which alternative offers better performance. The final results have been obtained taking into account the proposed problem and that the environment has not been changed, that is to say, the environment has not been altered to perform the tests.
Year
DOI
Venue
2016
10.3390/s16081180
SENSORS
Keywords
Field
DocType
object detection,object classification,shapes descriptors,Support Vector Machine,mobile robots,robot navigation
Bag-of-words model,Computer vision,Object detection,Machine vision,Support vector machine,Artificial intelligence,RGB color model,Geometric shape,Engineering,Mobile robot navigation,Mobile robot
Journal
Volume
Issue
Citations 
16
8.0
5
PageRank 
References 
Authors
0.45
3
4
Name
Order
Citations
PageRank
Alejandra Carolina Hernández165.54
Clara Gómez264.86
Jonathan Crespo3194.10
R. Barber4658.93