COLLABORATIVE NAVIGATION SIMULATION TOOL USING KALMAN FILTER WITH IMPLICIT CONSTRAINTS
| Autor: | Garcia-Fernandez, N., Alkhatib, H., Schön, S., Vosselman, G., Oude Elberink, S.J., Yang, M.Y. |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Dewey Decimal Classification::500 | Naturwissenschaften::550 | Geowissenschaften
Sensor configurations lcsh:Applied optics. Photonics Accuracy and precision 010504 meteorology & atmospheric sciences Laser scanning Computer science Monte Carlo method Real-time computing 020101 civil engineering 02 engineering and technology Positioning techniques 01 natural sciences lcsh:Technology 0201 civil engineering Extended Kalman filter ddc:550 Intelligent systems Localization problems Single vehicle Implicit constraints Konferenzschrift 0105 earth and related environmental sciences Extended Kalman filters Multi-sensor fusion Multi-sensor systems Plane (geometry) lcsh:T Intelligent decision support system Monte Carlo Simulation lcsh:TA1501-1820 Monte Carlo methods Kalman filter Collaborative Navigation Navigation Extended Kalman Filter (EKF) lcsh:TA1-2040 lcsh:Engineering (General). Civil engineering (General) |
| Zdroj: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol IV-2-W5, Pp 559-566 (2019) ISPRS Geospatial Week 2019 : 10-14 June 2019, Enschede, The Netherlands ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; IV-2/W5 |
| ISSN: | 2194-9050 2194-9042 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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