Volume IV-2/W5
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 559-566, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W5-559-2019
© 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, 559-566, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W5-559-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  29 May 2019

29 May 2019

COLLABORATIVE NAVIGATION SIMULATION TOOL USING KALMAN FILTER WITH IMPLICIT CONSTRAINTS

N. Garcia-Fernandez1, H. Alkhatib2, and S. Schön1 N. Garcia-Fernandez et al.
  • 1Institut für Erdmessung (IfE), Leibniz Universität Hannover, Germany
  • 2Geodätisches Institut Hannover, Leibniz Universität Hannover, Germany

Keywords: Collaborative Navigation, Multi-sensor fusion, Monte Carlo Simulation, Extended Kalman Filter (EKF)

Abstract. Collaborative Positioning (CP) is a networked positioning technique in which different multi-sensor systems (nodes) enhance the accuracy and precision of these navigation solutions by performing measurements or by sharing information (links) between each other. The wide spectrum of available sensors that are used in these complex scenarios bring the necessity to analyze the sensibility of the system to different configurations in order to find optimal solutions. In this paper, we discuss the implementation and evaluation of a simulation tool that allows us to study these questions. The simulation tool is successfully implemented as a plane based localization problem, in which the sensor measurements are fused in a Collaborative Extended Kalman Filter (C-EKF) algorithm with implicit constraints. Using a real urban scenario with three vehicles equipped with various positioning sensors, the impact of the sensor configuration is investigated and discussed by intensive Monte Carlo simulations. The results show the influence of the laser scanner measurements on the accuracy and precision of the estimation, and the increased performance of the collaborative navigation techniques with respect to the single vehicle method.