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 dif...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.展开更多
文摘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.
