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.
Highlights
Navigating in indoor environments inevitably requires detection and crossing doors that are regarded as integral parts of any indoor setting, in human habitats
The second stage presents the results of other modules the system detects the door via convolutional neural networks (CNN)-Social Robot Indoor Navigation (SRIN) [1]
We focused on addressing a doorway detection algorithm that will be used in indoor navigation for social robots with limited sensors
Summary
Navigating in indoor environments inevitably requires detection and crossing doors that are regarded as integral parts of any indoor setting, in human habitats (homes). Whereas this task does not require much effort for humans and even their pets, it is a challenge for social and other autonomous robots. The capacity to detect doors and their orientation are critical for any navigation system and are the main subject of this paper; though the related problem of passing through a door is not within the scope of this study. This research question has attracted attention by many researchers on robotics and as we shall discuss in Section 2, the detection and navigation through a doorway are mostly addressed via sensor fusion techniques, deployment of rather expensive built-in sensor(s) on-board the robot, or augmenting the environment by appropriate and dedicated sensors or Quick Response (QR) Codes
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