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