Monte Carlo variance propagation for the uncertainty modeling of a kinematic LiDAR-based multi-sensor system

authored by: Dominik Ernst, Sören Vogel, Hamza Alkhatib, Ingo Neumann
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.
Organisation(s): Geodetic Institute
Type: Article
Journal: Journal of Applied Geodesy
Volume: 18
Pages: 237-252
No. of pages: 16
ISSN: 1862-9016
Publication date: 04.04.2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Engineering (miscellaneous), Earth and Planetary Sciences (miscellaneous), Modelling and Simulation
Electronic version(s): https://doi.org/10.1515/jag-2022-0033 (Access: Closed)

BibTeX

@article{76359474cf8a424084e8cc05fb1a30dc,
title = "Monte Carlo variance propagation for the uncertainty modeling of a kinematic LiDAR-based multi-sensor system",
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",
author = "Dominik Ernst and S{\"o}ren Vogel and Hamza Alkhatib and Ingo Neumann",
year = "2024",
month = apr,
day = "4",
doi = "10.1515/jag-2022-0033",
language = "English",
volume = "18",
pages = "237--252",
journal = "Journal of Applied Geodesy",
issn = "1862-9016",
publisher = "Walter de Gruyter GmbH",
number = "2",
}