Reference : Faster Visual-Based Localization with Mobile-PoseNet

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/41615

Title :	Faster Visual-Based Localization with Mobile-PoseNet
Language :	English
Author, co-author :	Cimarelli, Claudio [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
	Cazzato, Dario [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
	Olivares Mendez, Miguel Angel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Voos, Holger [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Engineering Research Unit]
Publication date :	22-Aug-2019
Main document title :	International Conference on Computer Analysis of Images and Patterns
Pages :	219--230
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	International Conference on Computer Analysis of Images and Patterns
Event date :	from 3-09-2019 to 5-09-2019
Event organizer :	Springer
Event place (city) :	Salerno
Event country :	Italy
Keywords :	[en] Deep Learning ; Convolutional Neural Networks ; 6-DoF Pose Estimation ; Visual Based Localization ; UAV
Abstract :	[en] Precise and robust localization is of fundamental importance for robots required to carry out autonomous tasks. Above all, in the case of Unmanned Aerial Vehicles (UAVs), efficiency and reliability are critical aspects in developing solutions for localization due to the limited computational capabilities, payload and power constraints. In this work, we leverage novel research in efficient deep neural architectures for the problem of 6 Degrees of Freedom (6-DoF) pose estimation from single RGB camera images. In particular, we introduce an efficient neural network to jointly regress the position and orientation of the camera with respect to the navigation environment. Experimental results show that the proposed network is capable of retaining similar results with respect to the most popular state of the art methods while being smaller and with lower latency, which are fundamental aspects for real-time robotics applications.
Permalink :	http://hdl.handle.net/10993/41615

	File(s) associated to this reference
	Fulltext file(s): File Commentary Version Size Access Open access CAIP19_mobiposenet_cameraready.pdf Author preprint 7.77 MB View/Open

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