Abstract
This paper considers the use of a low cost mobile device in order to develop a mobile mapping system (MMS), which exploits only sensors embedded in the device. The goal is to make this MMS usable and reliable even in difficult environments (e.g. emergency conditions, when also WiFi connection might not work). For this aim, a navigation system able to deal with the unavailability of the GNSS (e.g. indoors) is proposed first. Positioning is achieved by a pedestrian dead reckoning approach, i.e. a specific particle filter has been designed to enable good position estimations by a small number of particles (e.g. 100). This specific characteristic enables its real time use on the standard mobile devices. Then, 3D reconstruction of the scene can be achieved by processing multiple images acquired with the standard camera embedded in the device. As most of the vision-based 3D reconstruction systems are recently proposed in the literature, this work considers the use of structure from motion to estimate the geometrical structure of the scene. The detail level of the reconstructed scene is clearly related to the number of images processed by the reconstruction system. However, the execution of a 3D reconstruction algorithm on a mobile device imposes several restrictions due to the limited amount of available energy and computing power. This consideration motivates the search for new methods to obtain similar results with less computational cost. This paper proposes a novel method for feature matching, which allows increasing the number of correctly matched features between two images according to our simulations and can make the matching process more robust.
Highlights
IntroductionThanks to the continuous increase of applications using geo-spatial data (Guarnieri et al 2015; Habib et al 2005; Pirotti et al 2015; Remondino, Guarnieri, and Vettore 2005), in the last decades several mobile mapping systems (MMSs) have been developed, mostly based on the use of terrestrial or airborne vehicles (Chiang, Noureldin, and El-Sheimy 2003; Kraus and Pfeifer 1998; Pirotti et al 2014; Remondino et al 2011; Toth 2001; Toth and Grejner-Brzezinska 1997), equipped with remote sensing instruments such as laser scanners and cameras.MMSs have become quite popular even among the general public due to the success of web tools which allow street view navigation
This paper considers the use of a low cost mobile device in order to develop a mobile mapping system (MMS), which exploits only sensors embedded in the device
This paper proposes a novel method for feature matching, which allows increasing the number of correctly matched features between two images according to our simulations and can make the matching process more robust
Summary
Thanks to the continuous increase of applications using geo-spatial data (Guarnieri et al 2015; Habib et al 2005; Pirotti et al 2015; Remondino, Guarnieri, and Vettore 2005), in the last decades several mobile mapping systems (MMSs) have been developed, mostly based on the use of terrestrial or airborne vehicles (Chiang, Noureldin, and El-Sheimy 2003; Kraus and Pfeifer 1998; Pirotti et al 2014; Remondino et al 2011; Toth 2001; Toth and Grejner-Brzezinska 1997), equipped with remote sensing instruments such as laser scanners and cameras.MMSs have become quite popular even among the general public due to the success of web tools which allow street view navigation. Thanks to the continuous increase of applications using geo-spatial data (Guarnieri et al 2015; Habib et al 2005; Pirotti et al 2015; Remondino, Guarnieri, and Vettore 2005), in the last decades several mobile mapping systems (MMSs) have been developed, mostly based on the use of terrestrial or airborne vehicles (Chiang, Noureldin, and El-Sheimy 2003; Kraus and Pfeifer 1998; Pirotti et al 2014; Remondino et al 2011; Toth 2001; Toth and Grejner-Brzezinska 1997), equipped with remote sensing instruments such as laser scanners and cameras. The current generation of smartphones is typically embedded with several MEMS sensors (Schiavone, Desmulliez, and Walton 2014), which, typically provide quite noisy measurements
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