Development of an indoor navigation system for a monocular-vision-based autonomous mobile robot

2009 ◽  
Vol 14 (3) ◽  
pp. 324-328 ◽  
Author(s):  
Eiji Hayashi ◽  
Tomoyuki Kinoshita
Keyword(s):  
Mobile Robot ◽  
Navigation System ◽  
Indoor Navigation ◽  
Monocular Vision ◽  
Autonomous Mobile Robot ◽  
Indoor Navigation System

scholarly journals Semantic Segmentation to Develop an Indoor Navigation System for an Autonomous Mobile Robot

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 855
Author(s):  
Daniel Teso-Fz-Betoño ◽  
Ekaitz Zulueta ◽  
Ander Sánchez-Chica ◽  
Unai Fernandez-Gamiz ◽  
Aitor Saenz-Aguirre
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Navigation System ◽  
Reaction Times ◽  
Semantic Segmentation ◽  
Indoor Navigation ◽  
Autonomous Mobile Robot ◽  
The Neural Network ◽  
Indoor Navigation System ◽  
Real Scenario

In this study, a semantic segmentation network is presented to develop an indoor navigation system for a mobile robot. Semantic segmentation can be applied by adopting different techniques, such as a convolutional neural network (CNN). However, in the present work, a residual neural network is implemented by engaging in ResNet-18 transfer learning to distinguish between the floor, which is the navigation free space, and the walls, which are the obstacles. After the learning process, the semantic segmentation floor mask is used to implement indoor navigation and motion calculations for the autonomous mobile robot. This motion calculations are based on how much the estimated path differs from the center vertical line. The highest point is used to move the motors toward that direction. In this way, the robot can move in a real scenario by avoiding different obstacles. Finally, the results are collected by analyzing the motor duty cycle and the neural network execution time to review the robot’s performance. Moreover, a different net comparison is made to determine other architectures’ reaction times and accuracy values.

Indoor Navigation System of Omni-Directional Mobile Robot Based on Static Obstacles Avoidance

2020 ◽  
Vol 13 (1) ◽  
pp. 27
Author(s):  
Shaaban Ali Salman ◽  
Qais A. Khasawneh ◽  
Mohammad A. Jaradat ◽  
Mansour Y. Alramlawi
Keyword(s):  
Mobile Robot ◽  
Navigation System ◽  
Indoor Navigation ◽  
Indoor Navigation System

BIM Based Indoor Navigation System of Hermes Mobile Robot

2013 ◽  
pp. 375-382 ◽  
Author(s):  
Barbara Siemiątkowska ◽  
Bogdan Harasymowicz-Boggio ◽  
Maciej Przybylski ◽  
Monika Różańska-Walczuk ◽  
Mateusz Wiśniowski ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Navigation System ◽  
Indoor Navigation ◽  
Indoor Navigation System

Experimental research of indoor navigation system based on broadcast signals

2020 ◽  
Vol 49 (5) ◽  
pp. 49-57
Author(s):  
A. V. Ksendzuk ◽  
E. A. Surmin ◽  
V. V. Kachesov ◽  
S. O. Zhdanov ◽  
K. S. Shakhalov
Keyword(s):  
Navigation System ◽  
Open Space ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Estimation Accuracy ◽  
Satellite Systems ◽  
Indoor Navigation System ◽  
Point To Point ◽  
Navigation Signals ◽  
Navigation Equipment

Results of an experimental study of a local navigation system based on the processing signals from broadcast sources presented. The results of the development of processing algorithms for point-to-point coordinates estimation of the object are presented. The results of the development of algorithms for trajectories estimation are presented. In performed simulation the possibility of obtaining submeter position estimation accuracy in the proposed system is shown. Development results of the navigation module demonstrator are presented. The results of experimental work in difficult navigation conditions, in the presence of shading, reflections and other factors, are presented. It is shown that the developed navigation module allows in the open space near buildings which partially obscuring the satellite systems signals to obtain accuracy higher than the GNSS navigation equipment. In indoor environment in the absence of satellite navigation signals, the developed module shows positioning accuracy not worse than 1.5 meters and provides a measurement rate 1 Hz and better.

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