Volume I-3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-3, 13-18, 2012
https://doi.org/10.5194/isprsannals-I-3-13-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-3, 13-18, 2012
https://doi.org/10.5194/isprsannals-I-3-13-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  13 Jul 2012

13 Jul 2012

SEMI-AUTOMATIC CO-REGISTRATION OF PHOTOGRAMMETRIC AND LIDAR DATA USING BUILDINGS

C. Armenakis, Y. Gao, and G. Sohn C. Armenakis et al.
  • Geomatics Engineering, GeoICT Lab, Department of Earth and Space Science and Engineering, York University, Toronto, Ontario, Canada

Keywords: DEM, LiDAR, Co-registration, Automation, Matching, Buildings, Planes, Points, Transformations

Abstract. In this work, the co-registration steps between LiDAR and photogrammetric DSM 3Ddata are analyzed and a solution based on automated plane matching is proposed and implemented. For a robust 3D geometric transformation both planes and points are used. Initially planes are chosen as the co-registration primitives. To confine the search space for the plane matching a sequential automatic building matching is performed first. For matching buildings from the LiDAR and the photogrammetric data, a similarity objective function is formed based on the roof height difference (RHD), the 3D histogram of the building attributes, and the building boundary area of a building. A region growing algorithm based on a Triangulated Irregular Network (TIN) is implemented to extract planes from both datasets. Next, an automatic successive process for identifying and matching corresponding planes from the two datasets has been developed and implemented. It is based on the building boundary region and determines plane pairs through a robust matching process thus eliminating outlier pairs. The selected correct plane pairs are the input data for the geometric transformation process. The 3D conformal transformation method in conjunction with the attitude quaternion is applied to obtain the transformation parameters using the normal vectors of the corresponding plane pairs. Following the mapping of one dataset onto the coordinate system of the other, the Iterative Closest Point (ICP) algorithm is then applied, using the corresponding building point clouds to further refine the transformation solution. The results indicate that the combination of planes and points improve the co-registration outcomes.