Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 109-116, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-109-2018
© 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, 109-116, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-109-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

DEM BASED REGISTRATION OF MULTI-SENSOR AIRBORNE POINT CLOUDS EXEMPLARY SHOWN ON A RIVER SIDE IN NON URBAN AREA

R. Boerner1, Y. Xu1, L. Hoegner1, R. Baran2, F. Steinbacher2, and U. Stilla1 R. Boerner et al.
  • 1Photogrammetry and Remote Sensing, Technical University of Munich 80333 Munich, Germany
  • 2SteinbacherConsult Ingenieurgesellschaft mbH & Co. KG 86356 Neusäß/ Augsburg, Germany

Keywords: airborne point clouds, registration, 2.5D DEM, multi sensor

Abstract. This paper presents a method to register photogrammetric point clouds generated from optical images acquired by UAV and aerial LIDAR point clouds. Normally, the registration of two airborne scans of the same scene is solved by the use of control points and the direct registration using GNSS and INS information. However, the registration of multi-sensor point clouds without control points is more complicated and challenging. For the scene of non urban areas, the registration task gets even more complicated, because it is hard to extract sufficient geometric primitives from the building structures. For our proposed method, an outdoor scene is tested providing almost no man-made objects. Therefore, it is nearly impossible to search for planar objects and use them for registration. With no geometric primitives extracted, the proposed method utilizes the structure of the 2.5D DEM created from the ground points of the point cloud. Besides, instead of using control points or key points, the method automatic detect key planes from the 2.5D DEM as correspondences. These key planes are detected on a regular grid by the use of a predefined mask. To mark a DEM grid cell as key plane the histogram of sums of the angles between the center cell is used. Afterwards, similarity values between two key planes are calculated based on the histogram differences and a RANSAC based strategy is adopted to find corresponding key planes and estimate the transformation parameters. Experiments conducted in this paper indicate that it is feasible to register multi sensor point clouds with a big difference in their ground sampling distances with respect to the used cell size of the 2.5D DEM.