Georeferencing of an Unmanned Aerial System by Means of an Iterated Extended Kalman Filter Using a 3D City Model

Bureick, Johannes; Vogel, Sören; Neumann, Ingo; Unger, Jakob; Alkhatib, Hamza

doi:10.1007/s41064-019-00084-x

Cited by 10 publications

(17 citation statements)

References 30 publications

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“…Although the matching approach is in general similar to ours, we focus on the applicability of official 3D models, which are available on the fly via geo‐web services. Matching with other known features of the environment has been presented in Bureick, Vogel, Neumann, Unger, and Alkhatib (2019). Here, the scan lines of a unmanned aerial vehicle (UAV) based laser scanner and plane parameters from a simulated city model are used to create constraints, which are used in an iterative Kalman filter for the trajectory estimation of the UAV.…”

Section: Related Workmentioning

confidence: 99%

Improving trajectory estimation using 3D city models and kinematic point clouds

Lucks

Klingbeil

Plümer

et al. 2021

Transactions in GIS

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Accurate and robust positioning of vehicles in urban environments is of high importance for autonomous driving or mobile mapping. In mobile mapping systems, a simultaneous mapping of the environment using laser scanning and an accurate positioning using global navigation satellite systems are targeted. This requirement is often not guaranteed in shadowed cities where global navigation satellite system signals are usually disturbed, weak or even unavailable. We propose a novel approach which incorporates prior knowledge (i.e., a 3D city model of the environment) and improves the trajectory. The recorded point cloud is matched with the semantic city model using a point‐to‐plane iterative closest point method. A pre‐classification step enables an informed sampling of appropriate matching points. Random forest is used as classifier to discriminate between facade and remaining points. Local inconsistencies are tackled by a segmentwise partitioning of the point cloud where an interpolation guarantees a seamless transition between the segments. The general applicability of the method implemented is demonstrated on an inner‐city data set recorded with a mobile mapping system.

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Section: Related Workmentioning

confidence: 99%

Improving trajectory estimation using 3D city models and kinematic point clouds

Lucks

Klingbeil

Plümer

et al. 2021

Transactions in GIS

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“…In [28,29], such IEKF algorithms with GHM were used. Nonetheless, for the first time in the context of MSS georeferencing, the authors in [30] introduced such a combination of IEKF with implicit measurement equations, which was then applied by [31], the authors in [1,32] for localizing different kinds of MSS in various environments. Sometimes, the system behavior or environmental conditions might vary over time causing the system or observation model(s) to change.…”

Section: Georeferencing By Means Of Filtering Techniquesmentioning

confidence: 99%

“…In general, low-cost GNSS receivers deliver positions with accuracy at the meter level, unless good GNSS conditions are available and differential techniques are applied, which can increase the accuracy up to the decimeter or even centimeter level [1]. On the other hand, IMUs are always subject to drifting, which makes their data unreliable over time.…”

Section: Introductionmentioning

confidence: 99%

“…A lightweight and low-cost IMU delivers the roll and pitch angles with an accuracy of 0.1°and the heading angle with an accuracy of 0.8° [1]. According to [5], a minimal accelerometer bias of one least significant bit could lead to a position error of around 200 m in a period of 100 s. Therefore, the development of appropriate algorithms that can best compensate for such possible errors or losses in the GNSS and IMU datasets and, thus, will increase the accuracy of georeferencing is an inevitable request that should be fulfilled.…”

Section: Introductionmentioning

confidence: 99%

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Information-Based Georeferencing of an Unmanned Aerial Vehicle by Dual State Kalman Filter with Implicit Measurement Equations

et al. 2021

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Georeferencing a kinematic Multi-Sensor-System (MSS) within crowded areas, such as inner-cities, is a challenging task that should be conducted in the most reliable way possible. In such areas, the Global Navigation Satellite System (GNSS) data either contain inevitable errors or are not continuously available. Regardless of the environmental conditions, an Inertial Measurement Unit (IMU) is always subject to drifting, and therefore it cannot be fully trusted over time. Consequently, suitable filtering techniques are required that can compensate for such possible deficits and subsequently improve the georeferencing results. Sometimes it is also possible to improve the filter quality by engaging additional complementary information. This information could be taken from the surrounding environment of the MSS, which usually appears in the form of geometrical constraints. Since it is possible to have a high amount of such information in an environment of interest, their consideration could lead to an inefficient filtering procedure. Hence, suitable methodologies are necessary to be extended to the filtering framework to increase the efficiency while preserving the filter quality. In the current paper, we propose a Dual State Iterated Extended Kalman Filter (DSIEKF) that can efficiently georeference a MSS by taking into account additional geometrical information. The proposed methodology is based on implicit measurement equations and nonlinear geometrical constraints, which are applied to a real case scenario to further evaluate its performance.

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“…In addition to control points, area-based evaluation methods are widespread. In this respect, existing 3D city models can be utilized as a reference for evaluation [60][61][62]. Furthermore, reference point clouds (e.g., from TLS) are commonly used for evaluation (e.g., [10,11,18,21,33,[63][64][65]), since software packages provide algorithms for the comparison of two point clouds.…”

Section: Evaluation Of Mobile Laser Scanning Systemsmentioning

confidence: 99%

Design and Evaluation of a Permanently Installed Plane-Based Calibration Field for Mobile Laser Scanning Systems

et al. 2020

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Mobile laser scanning has become an established measuring technique that is used for many applications in the fields of mapping, inventory, and monitoring. Due to the increasing operationality of such systems, quality control w.r.t. calibration and evaluation of the systems becomes more and more important and is subject to on-going research. This paper contributes to this topic by using tools from geodetic configuration analysis in order to design and evaluate a plane-based calibration field for determining the lever arm and boresight angles of a 2D laser scanner w.r.t. a GNSS/IMU unit (Global Navigation Satellite System, Inertial Measurement Unit). In this regard, the impact of random, systematic, and gross observation errors on the calibration is analyzed leading to a plane setup that provides accurate and controlled calibration parameters. The designed plane setup is realized in the form of a permanently installed calibration field. The applicability of the calibration field is tested with a real mobile laser scanning system by frequently repeating the calibration. Empirical standard deviations of <1 ... 1.5 mm for the lever arm and <0.005 ∘ for the boresight angles are obtained, which was priorly defined to be the goal of the calibration. In order to independently evaluate the mobile laser scanning system after calibration, an evaluation environment is realized consisting of a network of control points as well as TLS (Terrestrial Laser Scanning) reference point clouds. Based on the control points, both the horizontal and vertical accuracy of the system is found to be < 10 mm (root mean square error). This is confirmed by comparisons to the TLS reference point clouds indicating a well calibrated system. Both the calibration field and the evaluation environment are permanently installed and can be used for arbitrary mobile laser scanning systems.

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Georeferencing of an Unmanned Aerial System by Means of an Iterated Extended Kalman Filter Using a 3D City Model

Cited by 10 publications

References 30 publications

Improving trajectory estimation using 3D city models and kinematic point clouds

Improving trajectory estimation using 3D city models and kinematic point clouds

Information-Based Georeferencing of an Unmanned Aerial Vehicle by Dual State Kalman Filter with Implicit Measurement Equations

Design and Evaluation of a Permanently Installed Plane-Based Calibration Field for Mobile Laser Scanning Systems

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