KR-net: A dependable visual kidnap recovery network for indoor spaces

Janghun Hyeon, Dongwoo Kim, Bumchul Jang, Hyunga Choi, Dong Hoon Yi, Kyungho Yoo, Jeongae Choi, Nakju Doh

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

    5 Citations (Scopus)

    Abstract

    In this paper, we propose a dependable visual kidnap recovery (KR) framework that pinpoints a unique pose in a given 3D map when a device is turned on. For this framework, we first develop indoor-GeM (i-GeM), which is an extension of GeM [1] but considerably more robust than other global descriptors [2]-[4], including GeM itself. Then, we propose a convolutional neural network (CNN)-based system called KR-Net, which is based on a coarse-to-fine paradigm as in [5] and [6]. To our knowledge, KR-Net is the first network that can pinpoint a wake-up pose with a confidence level near 100% within a 1.0 m translational error boundary. This dependable success rate is enabled not only by i-GeM, but also by a combinatorial pooling approach that uses multiple images around the wake-up spot, whereas previous implementations [5], [6] were constrained to a single image. Experiments were conducted in two challenging datasets: a large-scale (12, 557 m2) area with frequent featureless or repetitive places and a place with significant view changes due to a one-year gap between prior modeling and query acquisition. Given 59 test query sets (eight images per pose), KR-Net successfully found all wake-up poses, with average and maximum errors of 0.246 m and 0.983 m, respectively.

    Original languageEnglish
    Title of host publication2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages8527-8533
    Number of pages7
    ISBN (Electronic)9781728162126
    DOIs
    Publication statusPublished - 2020 Oct 24
    Event2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020 - Las Vegas, United States
    Duration: 2020 Oct 242021 Jan 24

    Publication series

    NameIEEE International Conference on Intelligent Robots and Systems
    ISSN (Print)2153-0858
    ISSN (Electronic)2153-0866

    Conference

    Conference2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
    Country/TerritoryUnited States
    CityLas Vegas
    Period20/10/2421/1/24

    Bibliographical note

    Publisher Copyright:
    © 2020 IEEE.

    ASJC Scopus subject areas

    • Control and Systems Engineering
    • Software
    • Computer Vision and Pattern Recognition
    • Computer Science Applications

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