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.
Othman, K.M.; Rad, A.B. A Doorway Detection and Direction (3Ds) System for Social Robots via a Monocular Camera. Sensors 2020, 20, 2477. DOI: 10.3390/s20092477.
A Doorway Detection and Direction (3Ds) System for Social Robots via a Monocular Camera
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