Volume IV-4/W9
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W9, 35–42, 2019
https://doi.org/10.5194/isprs-annals-IV-4-W9-35-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W9, 35–42, 2019
https://doi.org/10.5194/isprs-annals-IV-4-W9-35-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Sep 2019

30 Sep 2019

IMPROVING GPS TRAJECTORIES USING 3D CITY MODELS AND KINEMATIC POINT CLOUDS

Y. Dehbi1, L. Lucks3, J. Behmann2, L. Klingbeil1, and L. Plümer4 Y. Dehbi et al.
  • 1Institute of Geodesy and Geoinformation, University of Bonn, Germany
  • 2Institute of Crop Sciences and Resource Protection, University of Bonn, Germany
  • 3Fraunhofer (IOSB) Institute of Optronics, System Technologies and Image Exploitation, Germany
  • 4Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China

Keywords: Mobile mapping system, ICP, City model, Classification, SVM, Point cloud, Trajectory

Abstract. Accurate and robust positioning of vehicles in urban environments is of high importance for many applications (e.g. autonomous driving or mobile mapping). In the case of mobile mapping systems, a simultaneous mapping of the environment using laser scanning and an accurate positioning using GNSS is targeted. This requirement is often not guaranteed in shadowed cities where GNSS signals are usually disturbed, weak or even unavailable. Both, the generated point clouds and the derived trajectory are consequently imprecise. We propose a novel approach which incorporates prior knowledge, i.e. 3D building model of the environment, and improves the point cloud and the trajectory. The key idea is to benefit from the complementarity of both GNSS and 3D building models. The point cloud is matched to the city model using a point-to-plane ICP. An informed sampling of appropriate matching points is enabled by a pre-classification step. Support vector machines (SVMs) are used to discriminate between facade and remaining points. Local inconsistencies are tackled by a segment-wise partitioning of the point cloud where an interpolation guarantees a seamless transition between the segments. The full processing chain is implemented from the detection of facades in the point clouds, the matching between them and the building models and the update of the trajectory estimate. The general applicability of the implemented method is demonstrated on an inner city data set recorded with a mobile mapping system.