On the use of INS to improve Feature Matching

• Main Authors: A. Masiero, A. Guarnieri, A. Vettore, F. Pirotti
• Format: Article
• Language: English
• Published: Copernicus Publications 2014-11-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-1/227/2014/isprsarchives-XL-1-227-2014.pdf
• ISSN: 1682-1750; 2194-9034

The continuous technological improvement of mobile devices opens the frontiers of Mobile Mapping systems to very compact systems, i.e. a smartphone or a tablet. This motivates the development of efficient 3D reconstruction techniques based on the sensors typically embedded in such devices, i.e. imaging sensors, GPS and Inertial Navigation System (INS). Such methods usually exploits photogrammetry techniques (structure from motion) to provide an estimation of the geometry of the scene. <br><br> Actually, 3D reconstruction techniques (e.g. structure from motion) rely on use of features properly matched in different images to compute the 3D positions of objects by means of triangulation. Hence, correct feature matching is of fundamental importance to ensure good quality 3D reconstructions. <br><br> Matching methods are based on the appearance of features, that can change as a consequence of variations of camera position and orientation, and environment illumination. For this reason, several methods have been developed in recent years in order to provide feature descriptors robust (ideally invariant) to such variations, e.g. Scale-Invariant Feature Transform (SIFT), Affine SIFT, Hessian affine and Harris affine detectors, Maximally Stable Extremal Regions (MSER). <br><br> This work deals with the integration of information provided by the INS in the feature matching procedure: a previously developed navigation algorithm is used to constantly estimate the device position and orientation. Then, such information is exploited to estimate the transformation of feature regions between two camera views. This allows to compare regions from different images but associated to the same feature as seen by the same point of view, hence significantly easing the comparison of feature characteristics and, consequently, improving matching. SIFT-like descriptors are used in order to ensure good matching results in presence of illumination variations and to compensate the approximations related to the estimation process.