Indoor Semantic Segmentation for Robot Navigating on Mobile

Wonsuk Kim; Junhee Seok

doi:10.1109/ICUFN.2018.8436956

Indoor Semantic Segmentation for Robot Navigating on Mobile

Wonsuk Kim
, Junhee Seok

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

51 Citations (Scopus)

Abstract

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 language	English
Title of host publication	ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks
Publisher	IEEE Computer Society
Pages	22-25
Number of pages	4
ISBN (Print)	9781538646465
DOIs	https://doi.org/10.1109/ICUFN.2018.8436956
Publication status	Published - 2018 Aug 14
Event	10th International Conference on Ubiquitous and Future Networks, ICUFN 2018 - Prague, Czech Republic Duration: 2018 Jul 3 → 2018 Jul 6

Publication series

Name	International Conference on Ubiquitous and Future Networks, ICUFN
Volume	2018-July
ISSN (Print)	2165-8528
ISSN (Electronic)	2165-8536

Other

Other	10th International Conference on Ubiquitous and Future Networks, ICUFN 2018
Country/Territory	Czech Republic
City	Prague
Period	18/7/3 → 18/7/6

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Atrous Convolution
Convolutional Neural Networks
Indoor Navigation
Semantic Segmentation

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture

Access to Document

10.1109/ICUFN.2018.8436956

Cite this

Indoor Semantic Segmentation for Robot Navigating on Mobile. / Kim, Wonsuk; Seok, Junhee.
ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks. IEEE Computer Society, 2018. p. 22-25 8436956 (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, W & Seok, J 2018, Indoor Semantic Segmentation for Robot Navigating on Mobile. in ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks., 8436956, International Conference on Ubiquitous and Future Networks, ICUFN, vol. 2018-July, IEEE Computer Society, pp. 22-25, 10th International Conference on Ubiquitous and Future Networks, ICUFN 2018, Prague, Czech Republic, 18/7/3. https://doi.org/10.1109/ICUFN.2018.8436956

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title = "Indoor Semantic Segmentation for Robot Navigating on Mobile",

abstract = "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.",

keywords = "Atrous Convolution, Convolutional Neural Networks, Indoor Navigation, Semantic Segmentation",

author = "Wonsuk Kim and Junhee Seok",

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ER -

Indoor Semantic Segmentation for Robot Navigating on Mobile

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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