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  but considerably more robust than other global descriptors -, 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  and . 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 ,  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.
|Title of host publication||2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||7|
|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
|Name||IEEE International Conference on Intelligent Robots and Systems|
|Conference||2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020|
|Period||20/10/24 → 21/1/24|
Bibliographical noteFunding Information:
This research was supported by the Brain Korea 21 Plus project in 2020.
© 2020 IEEE.
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Vision and Pattern Recognition
- Computer Science Applications