Details
| Original language | English |
|---|---|
| Journal | Sensors (Basel, Switzerland) |
| Volume | 19 |
| Issue number | 10 |
| Publication status | Published - 2 May 2019 |
Abstract
Georeferencing is an indispensable necessity regarding operating with kinematic multi-sensor systems (MSS) in various indoor and outdoor areas. Information from object space combined with various types of prior information (e.g., geometrical constraints) are beneficial especially in challenging environments where common solutions for pose estimation (e.g., global navigation satellite system or external tracking by a total station) are inapplicable, unreliable or inaccurate. Consequently, an iterated extended Kalman filter is used and a general georeferencing approach by means of recursive state estimation is introduced. This approach is open to several types of observation inputs and can deal with (non)linear systems and measurement models. The capability of using both explicit and implicit formulations of the relation between states and observations, and the consideration of (non)linear equality and inequality state constraints is a special feature. The framework presented is evaluated by an indoor kinematic MSS based on a terrestrial laser scanner. The focus here is on the impact of several different combinations of applied state constraints and the dependencies of two classes of inertial measurement units (IMU). The results presented are based on real measurement data combined with simulated IMU measurements.
Keywords
- georeferencing, implicit model, inequality state constraints, iterated extended Kalman filter, kinematic multi-sensor system, probability density function truncation
ASJC Scopus subject areas
- Chemistry(all)
- Analytical Chemistry
- Biochemistry, Genetics and Molecular Biology(all)
- Biochemistry
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Physics and Astronomy(all)
- Instrumentation
- Engineering(all)
- Electrical and Electronic Engineering
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In: Sensors (Basel, Switzerland), Vol. 19, No. 10, 02.05.2019.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Georeferencing of Laser Scanner-Based Kinematic Multi-Sensor Systems in the Context of Iterated Extended Kalman Filters Using Geometrical Constraints
AU - Vogel, Sören
AU - Alkhatib, Hamza
AU - Bureick, Johannes
AU - Moftizadeh, Rozhin
AU - Neumann, Ingo
N1 - Funding: This work was funded by the German Research Foundation (DFG) as part of the Research Training Group i.c.sens (RTG 2159) and NE 1453/5-1. 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 - 2019/5/2
Y1 - 2019/5/2
N2 - Georeferencing is an indispensable necessity regarding operating with kinematic multi-sensor systems (MSS) in various indoor and outdoor areas. Information from object space combined with various types of prior information (e.g., geometrical constraints) are beneficial especially in challenging environments where common solutions for pose estimation (e.g., global navigation satellite system or external tracking by a total station) are inapplicable, unreliable or inaccurate. Consequently, an iterated extended Kalman filter is used and a general georeferencing approach by means of recursive state estimation is introduced. This approach is open to several types of observation inputs and can deal with (non)linear systems and measurement models. The capability of using both explicit and implicit formulations of the relation between states and observations, and the consideration of (non)linear equality and inequality state constraints is a special feature. The framework presented is evaluated by an indoor kinematic MSS based on a terrestrial laser scanner. The focus here is on the impact of several different combinations of applied state constraints and the dependencies of two classes of inertial measurement units (IMU). The results presented are based on real measurement data combined with simulated IMU measurements.
AB - Georeferencing is an indispensable necessity regarding operating with kinematic multi-sensor systems (MSS) in various indoor and outdoor areas. Information from object space combined with various types of prior information (e.g., geometrical constraints) are beneficial especially in challenging environments where common solutions for pose estimation (e.g., global navigation satellite system or external tracking by a total station) are inapplicable, unreliable or inaccurate. Consequently, an iterated extended Kalman filter is used and a general georeferencing approach by means of recursive state estimation is introduced. This approach is open to several types of observation inputs and can deal with (non)linear systems and measurement models. The capability of using both explicit and implicit formulations of the relation between states and observations, and the consideration of (non)linear equality and inequality state constraints is a special feature. The framework presented is evaluated by an indoor kinematic MSS based on a terrestrial laser scanner. The focus here is on the impact of several different combinations of applied state constraints and the dependencies of two classes of inertial measurement units (IMU). The results presented are based on real measurement data combined with simulated IMU measurements.
KW - georeferencing
KW - implicit model
KW - inequality state constraints
KW - iterated extended Kalman filter
KW - kinematic multi-sensor system
KW - probability density function truncation
UR - http://www.scopus.com/inward/record.url?scp=85066428711&partnerID=8YFLogxK
U2 - 10.3390/s19102280
DO - 10.3390/s19102280
M3 - Article
C2 - 31108860
AN - SCOPUS:85066428711
VL - 19
JO - Sensors (Basel, Switzerland)
JF - Sensors (Basel, Switzerland)
SN - 1424-3210
IS - 10
ER -