Details
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS) |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1455-1464 |
| Number of pages | 10 |
| ISBN (electronic) | 9781538616475 |
| Publication status | Published - 7 Jun 2018 |
| Event | 2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018 - Monterey, United States Duration: 23 Apr 2018 → 26 Apr 2018 |
Abstract
Collaborative Positioning (CP) is a positioning technique in which a group of dynamic nodes (pedestrians, vehicles, etc.) equipped with different time synchronized sensors increase the quality of the Positioning, Navigation and Timing information (PNT) by exchanging navigation information as well as performing measurements between nodes or to elements of the environment such as urban furniture or buildings. The robustness of positioning is supposed to increase, describing an improvement in accuracy, integrity, continuity and availability compared to single node positioning, like e.g. standalone GNSS or tightly coupled GNSS + IMU solutions. In this paper, we describe the development of a realistic simulation tool for collaborative 3D navigation systems. Satellite navigation, inertial navigation and laser scanner techniques are combined in a Linearized Kalman Filter (LKF). Additionally, we discuss the use of available 3D building models with Level of Detail 2 (LoD2) or laser scanner point clouds as environmental models to generate the V2I measurements. We show the impact of the complex ratio between measurement precision and process noise on the estimated states and their precision.
Keywords
- Collaborative Positioning, LKF, LoD2, Multi-sensor systems, Point cloud, Simulation
ASJC Scopus subject areas
- Engineering(all)
- Automotive Engineering
- Engineering(all)
- Aerospace Engineering
- Mathematics(all)
- Control and Optimization
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2018 IEEE/ION Position, Location and Navigation Symposium (PLANS). Institute of Electrical and Electronics Engineers Inc., 2018. p. 1455-1464.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Evaluating a LKF simulation tool for collaborative navigation systems
AU - Fernandez, Nicolas Garcia
AU - Schön, Steffen
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].
PY - 2018/6/7
Y1 - 2018/6/7
N2 - Collaborative Positioning (CP) is a positioning technique in which a group of dynamic nodes (pedestrians, vehicles, etc.) equipped with different time synchronized sensors increase the quality of the Positioning, Navigation and Timing information (PNT) by exchanging navigation information as well as performing measurements between nodes or to elements of the environment such as urban furniture or buildings. The robustness of positioning is supposed to increase, describing an improvement in accuracy, integrity, continuity and availability compared to single node positioning, like e.g. standalone GNSS or tightly coupled GNSS + IMU solutions. In this paper, we describe the development of a realistic simulation tool for collaborative 3D navigation systems. Satellite navigation, inertial navigation and laser scanner techniques are combined in a Linearized Kalman Filter (LKF). Additionally, we discuss the use of available 3D building models with Level of Detail 2 (LoD2) or laser scanner point clouds as environmental models to generate the V2I measurements. We show the impact of the complex ratio between measurement precision and process noise on the estimated states and their precision.
AB - Collaborative Positioning (CP) is a positioning technique in which a group of dynamic nodes (pedestrians, vehicles, etc.) equipped with different time synchronized sensors increase the quality of the Positioning, Navigation and Timing information (PNT) by exchanging navigation information as well as performing measurements between nodes or to elements of the environment such as urban furniture or buildings. The robustness of positioning is supposed to increase, describing an improvement in accuracy, integrity, continuity and availability compared to single node positioning, like e.g. standalone GNSS or tightly coupled GNSS + IMU solutions. In this paper, we describe the development of a realistic simulation tool for collaborative 3D navigation systems. Satellite navigation, inertial navigation and laser scanner techniques are combined in a Linearized Kalman Filter (LKF). Additionally, we discuss the use of available 3D building models with Level of Detail 2 (LoD2) or laser scanner point clouds as environmental models to generate the V2I measurements. We show the impact of the complex ratio between measurement precision and process noise on the estimated states and their precision.
KW - Collaborative Positioning
KW - LKF
KW - LoD2
KW - Multi-sensor systems
KW - Point cloud
KW - Simulation
UR - http://www.scopus.com/inward/record.url?scp=85048899370&partnerID=8YFLogxK
U2 - 10.1109/plans.2018.8373539
DO - 10.1109/plans.2018.8373539
M3 - Conference contribution
AN - SCOPUS:85048899370
SP - 1455
EP - 1464
BT - 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018
Y2 - 23 April 2018 through 26 April 2018
ER -