Augmented Reality (AR) aims to enhance a person's vision of the real world with useful information about the surrounding environment. Amongst all the possible applications, AR systems can be very useful as visualization tools for structural and environmental monitoring. While the large majority of AR systems run on a laptop or on a head-mounted device, the advent of smartphones have created new opportunities. One of the most important functionality of an AR system is the ability of the device to self localize. This can be achieved through visual odometry, a very challenging task for smartphone. Indeed, on most of the available smartphone AR applications, self localization is achieved through GPS and/or inertial sensors. Hence, developing an AR system on a mobile phone also poses new challenges due to the limited amount of computational resources. In this paper we describe the development of a egomotion estimation algorithm for an Android smartphone. We also present an approach based on...
Visual-inertial tracking on Android for Augmented Reality applications / Porzi, Lorenzo; Ricci, Elisa; Ciarfuglia, Thomas A.; Zanin, Michele. - (2012), pp. 35-41. ( 2012 3rd IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, EESMS 2012 Perugia, ita 2012) [10.1109/EESMS.2012.6348402].
Visual-inertial tracking on Android for Augmented Reality applications
Ricci, Elisa;Zanin, Michele
2012-01-01
Abstract
Augmented Reality (AR) aims to enhance a person's vision of the real world with useful information about the surrounding environment. Amongst all the possible applications, AR systems can be very useful as visualization tools for structural and environmental monitoring. While the large majority of AR systems run on a laptop or on a head-mounted device, the advent of smartphones have created new opportunities. One of the most important functionality of an AR system is the ability of the device to self localize. This can be achieved through visual odometry, a very challenging task for smartphone. Indeed, on most of the available smartphone AR applications, self localization is achieved through GPS and/or inertial sensors. Hence, developing an AR system on a mobile phone also poses new challenges due to the limited amount of computational resources. In this paper we describe the development of a egomotion estimation algorithm for an Android smartphone. We also present an approach based on...I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione



