Details
| Original language | English |
|---|---|
| Pages (from-to) | 559-566 |
| Number of pages | 8 |
| Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume | 4 |
| Issue number | 2/W5 |
| Publication status | E-pub ahead of print - 29 May 2019 |
| Event | 4th ISPRS Geospatial Week 2019 - Enschede, Netherlands Duration: 10 Jun 2019 → 14 Jun 2019 |
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.
Keywords
- Collaborative Navigation, Extended Kalman Filter (EKF), Monte Carlo Simulation, Multi-sensor fusion
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
- Environmental Science(all)
- Environmental Science (miscellaneous)
- Physics and Astronomy(all)
- Instrumentation
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In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 4, No. 2/W5, 29.05.2019, p. 559-566.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - COLLABORATIVE NAVIGATION SIMULATION TOOL USING KALMAN FILTER with IMPLICIT CONSTRAINTS
AU - Garcia-Fernandez, N.
AU - Alkhatib, H.
AU - Schön, S.
N1 - Funding information: This work was supported by the German Research Foundation (DFG) as a part of the Research Training Group i.c.sens [GRK2159]. The computations were performed by the compute cluster, which is funded by the Leibniz Universität Hannover, the Lower Saxony Ministry of Science and Culture (MWK) and DFG.
PY - 2019/5/29
Y1 - 2019/5/29
N2 - 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.
AB - 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.
KW - Collaborative Navigation
KW - Extended Kalman Filter (EKF)
KW - Monte Carlo Simulation
KW - Multi-sensor fusion
UR - http://www.scopus.com/inward/record.url?scp=85067465815&partnerID=8YFLogxK
U2 - 10.5194/isprs-annals-IV-2-W5-559-2019
DO - 10.5194/isprs-annals-IV-2-W5-559-2019
M3 - Conference article
AN - SCOPUS:85067465815
VL - 4
SP - 559
EP - 566
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
IS - 2/W5
T2 - 4th ISPRS Geospatial Week 2019
Y2 - 10 June 2019 through 14 June 2019
ER -