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 language | English |
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Title of host publication | 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 8527-8533 |
Number of pages | 7 |
ISBN (Electronic) | 9781728162126 |
DOIs | |
Publication status | Published - 2020 Oct 24 |
Event | 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020 - Las Vegas, United States Duration: 2020 Oct 24 → 2021 Jan 24 |
Publication series
Name | IEEE International Conference on Intelligent Robots and Systems |
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ISSN (Print) | 2153-0858 |
ISSN (Electronic) | 2153-0866 |
Conference
Conference | 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020 |
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Country/Territory | United States |
City | Las Vegas |
Period | 20/10/24 → 21/1/24 |
Bibliographical note
Funding Information:This research was supported by the Brain Korea 21 Plus project in 2020.
Publisher Copyright:
© 2020 IEEE.
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Computer Vision and Pattern Recognition
- Computer Science Applications