Volume IV-2/W5
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 21-28, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W5-21-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 21-28, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W5-21-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  29 May 2019

29 May 2019

PRECISE VEHICLE RECONSTRUCTION FOR AUTONOMOUS DRIVING APPLICATIONS

M. Coenen, F. Rottensteiner, and C. Heipke M. Coenen et al.
  • Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Germany

Keywords: object detection, 3D reconstruction, 3D modelling, pose estimation, autonomous driving

Abstract. Interactive motion planing and collaborative positioning will play a key role in future autonomous driving applications. For this purpose, the precise reconstruction and pose estimation of other traffic participants, especially of other vehicles, is a fundamental task and will be tackled in this paper based on street level stereo images obtained from a moving vehicle. We learn a shape prior, consisting of vehicle geometry and appearance features, and we fit a vehicle model to initially detected vehicles. This is achieved by minimising an energy function, jointly incorporating 3D and 2D information to infer the model’s optimal and precise pose parameters. For evaluation we use the object detection and orientation benchmark of the KITTI dataset (Geiger et al., 2012). We can show a significant benefit of each of the individual energy terms of the overall objective function. We achieve good results with up to 94.8% correct and precise pose estimations with an average absolute error smaller than 3° for the orientation and 33 cm for position.