Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (electronic) | 9780578647098 |
| ISBN (print) | 978-1-7281-6830-2 |
| Publication status | Published - 2020 |
| Event | 23rd International Conference on Information Fusion, FUSION 2020 - Virtual, Pretoria, South Africa Duration: 6 Jul 2020 → 9 Jul 2020 |
Abstract
Multi-Sensor-System (MSS) georeferencing is a challenging task in engineering that should be dealt with in the most accurate way possible. The easiest and most straightforward way for this purpose is to rely on Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data. However, at indoor environments or crowded inner-city areas, such data are not accurate to be entirely relied on. Therefore, appropriate filtering algorithms are required to compensate for possible errors and to improve the accuracy of the results. Sometimes it is also possible to increase the functionality of a filtering technique by engaging additional complementary information that can directly influence the outputs. Such information could be, e.g. geometrical features of the environment in which the MSS runs through. The current paper deals with MSS georeferencing by means of a Dual State Iterated Extended Kalman Filter (DSIEKF) that is based on an efficient combination of the Iterated Extended Kalman Filter (IEKF) with implicit measurement equations technique and nonlinear geometrical constraints. Final results of such an algorithm are shown to be satisfactory not only from the accuracy point of view but also the computation time.
Keywords
- 6-DOF, Dual State, Geometrical constraints, Georeferencing, Iterated Extended Kalman Filter, Kalman filtering, Monte Carlo simulation, MSS
ASJC Scopus subject areas
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Information Systems
- Decision Sciences(all)
- Information Systems and Management
- Physics and Astronomy(all)
- Instrumentation
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Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9190414.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Information-based georeferencing by dual state iterated extended kalman filter with implicit measurement equations and nonlinear geometrical constraints
AU - Moftizadeh, Rozhin
AU - Bureick, Johannes
AU - Vogel, Sören
AU - Neumann, Ingo
AU - Alkhatib, Hamza
N1 - Funding information: ACKNOWLEDGMENT This research was funded by the Deutsche Forschungsge-meinschaft (DFG, German Research Foundation) - NE 1453/5-1 and as part of the Research Training Group i.c.sens [RTG 2159]. The computations were performed by the compute cluster, which is funded by the Leibniz University of Hanover, the Lower Saxony Ministry of Science and Culture (MWK) and DFG. This research was funded by the German Research Foundation (DFG) as part of the Research Training Group [RTG 2159].
PY - 2020
Y1 - 2020
N2 - Multi-Sensor-System (MSS) georeferencing is a challenging task in engineering that should be dealt with in the most accurate way possible. The easiest and most straightforward way for this purpose is to rely on Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data. However, at indoor environments or crowded inner-city areas, such data are not accurate to be entirely relied on. Therefore, appropriate filtering algorithms are required to compensate for possible errors and to improve the accuracy of the results. Sometimes it is also possible to increase the functionality of a filtering technique by engaging additional complementary information that can directly influence the outputs. Such information could be, e.g. geometrical features of the environment in which the MSS runs through. The current paper deals with MSS georeferencing by means of a Dual State Iterated Extended Kalman Filter (DSIEKF) that is based on an efficient combination of the Iterated Extended Kalman Filter (IEKF) with implicit measurement equations technique and nonlinear geometrical constraints. Final results of such an algorithm are shown to be satisfactory not only from the accuracy point of view but also the computation time.
AB - Multi-Sensor-System (MSS) georeferencing is a challenging task in engineering that should be dealt with in the most accurate way possible. The easiest and most straightforward way for this purpose is to rely on Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data. However, at indoor environments or crowded inner-city areas, such data are not accurate to be entirely relied on. Therefore, appropriate filtering algorithms are required to compensate for possible errors and to improve the accuracy of the results. Sometimes it is also possible to increase the functionality of a filtering technique by engaging additional complementary information that can directly influence the outputs. Such information could be, e.g. geometrical features of the environment in which the MSS runs through. The current paper deals with MSS georeferencing by means of a Dual State Iterated Extended Kalman Filter (DSIEKF) that is based on an efficient combination of the Iterated Extended Kalman Filter (IEKF) with implicit measurement equations technique and nonlinear geometrical constraints. Final results of such an algorithm are shown to be satisfactory not only from the accuracy point of view but also the computation time.
KW - 6-DOF
KW - Dual State
KW - Geometrical constraints
KW - Georeferencing
KW - Iterated Extended Kalman Filter
KW - Kalman filtering
KW - Monte Carlo simulation
KW - MSS
UR - http://www.scopus.com/inward/record.url?scp=85092694726&partnerID=8YFLogxK
U2 - 10.23919/fusion45008.2020.9190414
DO - 10.23919/fusion45008.2020.9190414
M3 - Conference contribution
AN - SCOPUS:85092694726
SN - 978-1-7281-6830-2
BT - Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd International Conference on Information Fusion, FUSION 2020
Y2 - 6 July 2020 through 9 July 2020
ER -