Evaluating a LKF simulation tool for collaborative navigation systems

authored by: Nicolas Garcia Fernandez, Steffen Schön
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.
Organisation(s): Institute of Geodesy
Type: Conference contribution
Pages: 1455-1464
No. of pages: 10
Publication date: 07.06.2018
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Automotive Engineering, Aerospace Engineering, Control and Optimization
Electronic version(s): https://doi.org/10.1109/plans.2018.8373539 (Access: Closed)
: Details in the research portal "Research@Leibniz University"

BibTeX

@inproceedings{31b49eec9def457abbffaba723412935,
title = "Evaluating a LKF simulation tool for collaborative navigation systems",
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",
author = "Fernandez, {Nicolas Garcia} and Steffen Sch{\"o}n",
note = "Funding information: This work was supported by the German Research Foundation (DFG) as a part of the Research Training Group i.c.sens [GRK2159].; 2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018 ; Conference date: 23-04-2018 Through 26-04-2018",
year = "2018",
month = jun,
day = "7",
doi = "10.1109/plans.2018.8373539",
language = "English",
pages = "1455--1464",
booktitle = "2018 IEEE/ION Position, Location and Navigation Symposium (PLANS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
}