Details
| Original language | English |
|---|---|
| Pages (from-to) | 237-252 |
| Number of pages | 16 |
| Journal | Journal of Applied Geodesy |
| Volume | 18 |
| Issue number | 2 |
| Early online date | 7 Sept 2023 |
| Publication status | Published - 4 Apr 2024 |
Abstract
Kinematic multi-sensor systems (MSS) are widely used for various applications, like mobile mapping or for autonomous systems. Depending on the application, insufficient knowledge of a system, like wrong assumptions about the accuracy of calibrations, might lead to inaccurate maps for mapping tasks or it might endanger humans in the context of autonomous driving. Uncertainty modeling can help to gain knowledge about the data captured by a system. Usually, uncertainty estimations for MSSs are done as backward modeling based on a comparison to reference datasets. In this paper, a forward modeling approach for the uncertainty modeling of a LiDAR-based kinematic MSS is chosen to estimate the uncertainty of an acquired point cloud. The MSS consists of a Leica Absolute Tracker and a platform with a 6-DoF sensor and Velodyne VLP-16 LiDAR. Results of multiple calibrations are used as the source for the uncertainty information for a Monte Carlo (MC) variance propagation of the point uncertainties. The deviations of the acquired point clouds in comparison to a ground truth can be decreased by an ensemble referencing process using the MC samples. Furthermore, the predicted uncertainties for the point clouds are well representing the actual deviations for reference panels closer to the system. Panels farther away indicate remaining distance depending effects.
Keywords
- Kinematic Laser Scanning, variance propagation, Uncertainty modeling, Monte Carlo Method, GUM, Multi-sensor system, Monte Carlo method, uncertainty modeling, kinematic laser scanning, multi-sensor system
ASJC Scopus subject areas
- Engineering(all)
- Engineering (miscellaneous)
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
- Mathematics(all)
- Modelling and Simulation
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In: Journal of Applied Geodesy, Vol. 18, No. 2, 04.04.2024, p. 237-252.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Monte Carlo variance propagation for the uncertainty modeling of a kinematic LiDAR-based multi-sensor system
AU - Ernst, Dominik
AU - Vogel, Sören
AU - Alkhatib, Hamza
AU - Neumann, Ingo
N1 - The authors would like to thank Aaronkumar Ehambram and Raphael Voges of the Real Time Systems research group (RTS) of the Institute for Systems Engineering at the Leibniz University Hannover for providing access to the robot and the support with the measurements.
PY - 2024/4/4
Y1 - 2024/4/4
N2 - Kinematic multi-sensor systems (MSS) are widely used for various applications, like mobile mapping or for autonomous systems. Depending on the application, insufficient knowledge of a system, like wrong assumptions about the accuracy of calibrations, might lead to inaccurate maps for mapping tasks or it might endanger humans in the context of autonomous driving. Uncertainty modeling can help to gain knowledge about the data captured by a system. Usually, uncertainty estimations for MSSs are done as backward modeling based on a comparison to reference datasets. In this paper, a forward modeling approach for the uncertainty modeling of a LiDAR-based kinematic MSS is chosen to estimate the uncertainty of an acquired point cloud. The MSS consists of a Leica Absolute Tracker and a platform with a 6-DoF sensor and Velodyne VLP-16 LiDAR. Results of multiple calibrations are used as the source for the uncertainty information for a Monte Carlo (MC) variance propagation of the point uncertainties. The deviations of the acquired point clouds in comparison to a ground truth can be decreased by an ensemble referencing process using the MC samples. Furthermore, the predicted uncertainties for the point clouds are well representing the actual deviations for reference panels closer to the system. Panels farther away indicate remaining distance depending effects.
AB - Kinematic multi-sensor systems (MSS) are widely used for various applications, like mobile mapping or for autonomous systems. Depending on the application, insufficient knowledge of a system, like wrong assumptions about the accuracy of calibrations, might lead to inaccurate maps for mapping tasks or it might endanger humans in the context of autonomous driving. Uncertainty modeling can help to gain knowledge about the data captured by a system. Usually, uncertainty estimations for MSSs are done as backward modeling based on a comparison to reference datasets. In this paper, a forward modeling approach for the uncertainty modeling of a LiDAR-based kinematic MSS is chosen to estimate the uncertainty of an acquired point cloud. The MSS consists of a Leica Absolute Tracker and a platform with a 6-DoF sensor and Velodyne VLP-16 LiDAR. Results of multiple calibrations are used as the source for the uncertainty information for a Monte Carlo (MC) variance propagation of the point uncertainties. The deviations of the acquired point clouds in comparison to a ground truth can be decreased by an ensemble referencing process using the MC samples. Furthermore, the predicted uncertainties for the point clouds are well representing the actual deviations for reference panels closer to the system. Panels farther away indicate remaining distance depending effects.
KW - Kinematic Laser Scanning
KW - variance propagation
KW - Uncertainty modeling
KW - Monte Carlo Method
KW - GUM
KW - Multi-sensor system
KW - Monte Carlo method
KW - uncertainty modeling
KW - kinematic laser scanning
KW - multi-sensor system
UR - http://www.scopus.com/inward/record.url?scp=85170692802&partnerID=8YFLogxK
U2 - 10.1515/jag-2022-0033
DO - 10.1515/jag-2022-0033
M3 - Article
VL - 18
SP - 237
EP - 252
JO - Journal of Applied Geodesy
JF - Journal of Applied Geodesy
SN - 1862-9016
IS - 2
ER -