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Night-time indoor relocalization using depth image with Convolutional Neural Networks

Li, R and Liu, Q and Gui, J and Gu, D and Hu, H (2016) Night-time indoor relocalization using depth image with Convolutional Neural Networks. In: UNSPECIFIED, ? - ?.

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Abstract

© 2016 Chinese Automation and Computing Society. In this work, we present a Convolutional Neural Network(CNN) with depth images as its inputs to solve the relocalization problem of a moving platform in night-time indoor environment. The developed algorithm can estimate the camera pose in an end-to-end manner with 0.40m and 7.49° errors in real time during night. It does not require any geometric computation as it directly uses a CNN for 6 DOFs pose regression. The architecture and its encoding methods of depth images are discussed. The proposed method is also evaluated on benchmark datasets collected from a motion capture system in our lab.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2016 22nd International Conference on Automation and Computing, ICAC 2016: Tackling the New Challenges in Automation and Computing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 27 Feb 2017 16:33
Last Modified: 23 Jan 2019 00:17
URI: http://repository.essex.ac.uk/id/eprint/19044

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