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Multi-Objective Deep CNN for Outdoor Auto-Navigation

Published: 27 June 2018 Publication History

Abstract

Target-guided navigation establishes the foundation for efficiently addressing vision-based multi-agent coordination for robotics. This work proposes a multi-objective deep convolution network which consists of two parallel branches built atop a shared feature extractor. The proposed network is capable of concurrently constructing semantic maps while achieving efficient visual detection of a designated guider robot or landmark towards outdoor navigation. In order to achieve the low latency requirements of the navigation controller, the structure and parameters of the network have been meticulously designed to boost run-time performance. The model is trained and tested on an altered version of the Cityscape outdoor dataset. We further finetune using a collected dataset in order to improve generalization performance on unseen outdoor scenes. Experimental results on an outdoor navigation robot equipped with an RGBD camera and GPU mini PC verifies the feasibility of the model.

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  • (2019)Tourism application with CNN-Based Classification specialized for cultural informationProceedings of the 21st International Conference on Information Integration and Web-based Applications & Services10.1145/3366030.3366073(8-14)Online publication date: 2-Dec-2019

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  1. Multi-Objective Deep CNN for Outdoor Auto-Navigation

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    cover image ACM Other conferences
    ICDLT '18: Proceedings of the 2018 2nd International Conference on Deep Learning Technologies
    June 2018
    112 pages
    ISBN:9781450364737
    DOI:10.1145/3234804
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    In-Cooperation

    • Chongqing University of Posts and Telecommunications
    • University of Electronic Science and Technology of China: University of Electronic Science and Technology of China

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 27 June 2018

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    Author Tags

    1. Deeplab
    2. MobileNet
    3. Object detection
    4. Outdoor navigation
    5. Semantic segmentation
    6. Single-shot-detector

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    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    • NSFC
    • Science and Technology Planning Project of Guangzhou
    • Guangzhou University Innovation and Entrepreneurship Education Project
    • Science and Technology Planning Project of Guangdong

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    ICDLT '18

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    • (2019)Tourism application with CNN-Based Classification specialized for cultural informationProceedings of the 21st International Conference on Information Integration and Web-based Applications & Services10.1145/3366030.3366073(8-14)Online publication date: 2-Dec-2019

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