Indoor Semantic Segmentation for Robot Navigating on Mobile

Wonsuk Kim, Junhee Seok

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)


In recent years, there have been many successes of using Deep Convolutional Neural Networks (DCNNs) in the task of pixel-level classification (also called 'semantic image segmentation'). The advances in DCNN have led to the development of autonomous vehicles that can drive with no driver controls by using sensors like camera, LiDAR, etc. In this paper, we propose a practical method to implement autonomous indoor navigation based on semantic image segmentation using state-of-the-art performance model on mobile devices, especially Android devices. We apply a system called 'Mobile DeepLabv3', which uses atrous convolution when applying semantic image segmentation by using MobileNetV2 as a network backbone. The ADE20K dataset is used to train our models specific to indoor environments. Since this model is for robot navigating, we re-label 150 classes into 20 classes in order to easily classify obstacles and road. We evaluate the trade-offs between accuracy and computational complexity, as well as actual latency and the number of parameters of the trained models.

Original languageEnglish
Title of host publicationICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks
PublisherIEEE Computer Society
Number of pages4
ISBN (Print)9781538646465
Publication statusPublished - 2018 Aug 14
Event10th International Conference on Ubiquitous and Future Networks, ICUFN 2018 - Prague, Czech Republic
Duration: 2018 Jul 32018 Jul 6


Other10th International Conference on Ubiquitous and Future Networks, ICUFN 2018
CountryCzech Republic


  • Atrous Convolution
  • Convolutional Neural Networks
  • Indoor Navigation
  • Semantic Segmentation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture

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