Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 471-477, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-471-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 471-477, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-471-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

A GUIDED REGISTRATION STRATEGY EMPLOYING VIRTUAL PLANES TO OVERCOME NON-STANDARD GEOMETRIES – USING THE EXAMPLE OF MOBILE MAPPING AND AERIAL OBLIQUE IMAGERY

P. Jende1, F. Nex1, M. Gerke2, and G. Vosselman1 P. Jende et al.
  • 1Dept. of Earth Observation Science, Faculty ITC, Universiteit Twente, The Netherlands
  • 2Institute of Geodesy and Photogrammetry, Technische Universität Braunschweig, Germany

Keywords: Mobile Mapping, Feature Matching, Image Orientation, Oblique Imagery

Abstract. Mobile mapping (MM) is an intriguing as well as emerging platform and technology for geo-data acquisition. In typical areas of interest for MM campaigns, such as urban areas, unwanted GNSS multipath, non-line-of-sight effects, and IMU drifts may lead to deteriorated position fixes. In this work, we are proposing a novel technique to register MM and aerial oblique imagery. As aerial platforms are not affected by GNSS occlusions and are able to collect very-high resolution images, a co-registration of the data sets enables a) an independent verification of the platform’s accuracy and b) an adjustment of the MM data’s pose. Both data sets depict the scene from an entirely different perspective, which complicates the matching problem. Our approach is based on the assumption that common visible entities in both images are available, e.g. façade surfaces. By determining planes coinciding with these visible entities in object space, variances can be overcome. As the orientation of the data sets is known – MM data has an unknown accuracy – derived planes are employed to support a visibility hypothesis while storing image information for image registration in object space. This enables constraining search space to support the actual registration. Although the inhomogeneity of the data sets poses a challenge to a successful registration, we can show that our stepwise strategy of finding common visible entities, exploiting them to increase the resemblance of the data sets, and utilising accurate registration methods renders this matching scenario possible. In this paper, the algorithm is explained in detail, experimental results of significant steps will be shown, and possible extensions are discussed.