The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-3/W4
17 Mar 2016
 | 17 Mar 2016


P. Jende, Z. Hussnain, M. Peter, S. Oude Elberink, M. Gerke, and G. Vosselman

Keywords: Mobile Mapping, Feature Extraction, Feature Matching, Image Orientation

Abstract. Mobile Mapping (MM) is a technique to obtain geo-information using sensors mounted on a mobile platform or vehicle. The mobile platform’s position is provided by the integration of Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS). However, especially in urban areas, building structures can obstruct a direct line-of-sight between the GNSS receiver and navigation satellites resulting in an erroneous position estimation. Therefore, derived MM data products, such as laser point clouds or images, lack the expected positioning reliability and accuracy. This issue has been addressed by many researchers, whose aim to mitigate these effects mainly concentrates on utilising tertiary reference data. However, current approaches do not consider errors in height, cannot achieve sub-decimetre accuracy and are often not designed to work in a fully automatic fashion. We propose an automatic pipeline to rectify MM data products by employing high resolution aerial nadir and oblique imagery as horizontal and vertical reference, respectively. By exploiting the MM platform’s defective, and therefore imprecise but approximate orientation parameters, accurate feature matching techniques can be realised as a pre-processing step to minimise the MM platform’s three-dimensional positioning error. Subsequently, identified correspondences serve as constraints for an orientation update, which is conducted by an estimation or adjustment technique. Since not all MM systems employ laser scanners and imaging sensors simultaneously, and each system and data demands different approaches, two independent workflows are developed in parallel.

Still under development, both workflows will be presented and preliminary results will be shown. The workflows comprise of three steps; feature extraction, feature matching and the orientation update. In this paper, initial results of low-level image and point cloud feature extraction methods will be discussed as well as an outline of the project and its framework will be given.