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

Daniel Teso-Fz-Betoño, Ekaitz Zulueta, Ander Sánchez-Chica, Unai Fernandez-Gamiz and Aitor Saenz-Aguirre
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Daniel Teso-Fz-Betoño: System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain
Ekaitz Zulueta: System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain
Ander Sánchez-Chica: System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain
Unai Fernandez-Gamiz: Department of Nuclear and Fluid Mechanics, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain
Aitor Saenz-Aguirre: Nuclear Engineering and Fluid Mechanics Department, University of the Basque Country (UPV/EHU), Avenida Otaola, 29, 20600 Eibar, Spain

Mathematics, 2020, vol. 8, issue 5, 1-19

Abstract: 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.

Keywords: indoor navigation; semantic segmentation; fully convolutional networks; obstacle detection; autonomous mobile robot; ResNet; Unet; Segnet (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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