A Doorway Detection and Direction (3Ds) System for Social Robots via a Monocular Camera

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ID: 115691
2020
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Abstract
In this paper, we propose a novel algorithm to detect a door and its orientation in indoor settings from the view of a social robot equipped with only a monocular camera. The challenge is to achieve this goal with only a 2D image from a monocular camera. The proposed system is designed through the integration of several modules, each of which serves a special purpose. The detection of the door is addressed by training a convolutional neural network (CNN) model on a new dataset for Social Robot Indoor Navigation (SRIN). The direction of the door (from the robot’s observation) is achieved by three other modules: Depth module, Pixel-Selection module, and Pixel2Angle module, respectively. We include simulation results and real-time experiments to demonstrate the performance of the algorithm. The outcome of this study could be beneficial in any robotic navigation system for indoor environments.
Reference Key
othman2020sensorsa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kamal M. Othman;Ahmad B. Rad;Othman, Kamal M.;Rad, Ahmad B.;
Journal sensors
Year 2020
DOI
10.3390/s20092477
URL
https://www.mdpi.com/1424-8220/20/9/2477
Keywords
Convolutional neural network social robot doorway detection doorway direction nao humanoid robot monocular camera 2d image depth information srin dataset robotic control system

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