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

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2477 ◽  
Author(s):  
Kamal M. Othman ◽  
Ahmad B. Rad
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Navigation System ◽  
Social Robot ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
Robotic Navigation ◽  
Monocular Camera ◽  
Simulation Results ◽  
Novel Algorithm

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.

scholarly journals Deep Learning-Based Positioning of Visually Impaired People in Indoor Environments

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6238
Author(s):  
Payal Mahida ◽  
Seyed Shahrestani ◽  
Hon Cheung
Keyword(s):  
Navigation System ◽  
Visually Impaired ◽  
Indoor Environment ◽  
Inertial Sensor ◽  
User Interaction ◽  
Indoor Navigation ◽  
Sensor Data ◽  
Indoor Environments ◽  
Visually Impaired People ◽  
Indoor Navigation System

Wayfinding and navigation can present substantial challenges to visually impaired (VI) people. Some of the significant aspects of these challenges arise from the difficulty of knowing the location of a moving person with enough accuracy. Positioning and localization in indoor environments require unique solutions. Furthermore, positioning is one of the critical aspects of any navigation system that can assist a VI person with their independent movement. The other essential features of a typical indoor navigation system include pathfinding, obstacle avoidance, and capabilities for user interaction. This work focuses on the positioning of a VI person with enough precision for their use in indoor navigation. We aim to achieve this by utilizing only the capabilities of a typical smartphone. More specifically, our proposed approach is based on the use of the accelerometer, gyroscope, and magnetometer of a smartphone. We consider the indoor environment to be divided into microcells, with the vertex of each microcell being assigned two-dimensional local coordinates. A regression-based analysis is used to train a multilayer perceptron neural network to map the inertial sensor measurements to the coordinates of the vertex of the microcell corresponding to the position of the smartphone. In order to test our proposed solution, we used IPIN2016, a publicly-available multivariate dataset that divides the indoor environment into cells tagged with the inertial sensor data of a smartphone, in order to generate the training and validating sets. Our experiments show that our proposed approach can achieve a remarkable prediction accuracy of more than 94%, with a 0.65 m positioning error.

scholarly journals An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation

2019 ◽  
Vol 9 (14) ◽  
pp. 2808 ◽  
Author(s):  
Yahui Peng ◽  
Xiaochen Liu ◽  
Chong Shen ◽  
Haoqian Huang ◽  
Donghua Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optical Flow ◽  
Navigation System ◽  
Moving Objects ◽  
Harmful Effect ◽  
Improved Method ◽  
Vision Navigation ◽  
Vehicle Velocity ◽  
Flow Algorithm

Aiming at enhancing the accuracy and reliability of velocity calculation in vision navigation, an improved method is proposed in this paper. The method integrates Mask-R-CNN (Mask Region-based Convolutional Neural Network) and K-Means with the pyramid Lucas Kanade algorithm in order to reduce the harmful effect of moving objects on velocity calculation. Firstly, Mask-R-CNN is used to recognize the objects which have motions relative to the ground and covers them with masks to enhance the similarity between pixels and to reduce the impacts of the noisy moving pixels. Then, the pyramid Lucas Kanade algorithm is used to calculate the optical flow value. Finally, the value is clustered by the K-Means algorithm to abandon the outliers, and vehicle velocity is calculated by the processed optical flow. The prominent advantages of the proposed algorithm are (i) decreasing the bad impacts to velocity calculation, due to the objects which have relative motions; (ii) obtaining the correct optical flow sets and velocity calculation outputs with less fluctuation; and (iii) the applicability enhancement of the optical flow algorithm in complex navigation environment. The proposed algorithm is tested by actual experiments. Results with superior precision and reliability show the feasibility and effectiveness of the proposed method for vehicle velocity calculation in vision navigation system.

scholarly journals Monocular Vision SLAM for Indoor Aerial Vehicles

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Koray Çelik ◽  
Arun K. Somani
Keyword(s):  
Indoor Navigation ◽  
Monocular Vision ◽  
Indoor Environments ◽  
Feature Points ◽  
Monocular Camera ◽  
Aerial Vehicles ◽  
Navigation Strategy ◽  
Aerial Vehicle ◽  
Technological Challenge ◽  
Limited Sensing

This paper presents a novel indoor navigation and ranging strategy via monocular camera. By exploiting the architectural orthogonality of the indoor environments, we introduce a new method to estimate range and vehicle states from a monocular camera for vision-based SLAM. The navigation strategy assumes an indoor or indoor-like manmade environment whose layout is previously unknown, GPS-denied, representable via energy based feature points, and straight architectural lines. We experimentally validate the proposed algorithms on a fully self-contained microaerial vehicle (MAV) with sophisticated on-board image processing and SLAM capabilities. Building and enabling such a small aerial vehicle to fly in tight corridors is a significant technological challenge, especially in the absence of GPS signals and with limited sensing options. Experimental results show that the system is only limited by the capabilities of the camera and environmental entropy.

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