Relative navigation of fixed‐wing aircraft in GPS‐denied environments

Ellingson, Gary; Brink, Kevin; McLain, Timothy W.

doi:10.1002/navi.364

Cited by 16 publications

(7 citation statements)

References 29 publications

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“…However, odometry does not have to rely solely on the measurement of wheel rotation. Movement can also be determined by observing the displacement of objects in successive frames of a video camera (Ellingson et al , 2020). On the other hand, Doppler velocity radar can be used to measure speed.…”

Section: Review Of Alternative Correction Methodsmentioning

confidence: 99%

GNSS denied navigation system for the manoeuvring flying objects

Borodacz,

Szczepański

2023

AEAT

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Purpose Before designing a navigation system, it is necessary to analyse possible approaches in terms of expected accuracy, existing limitations and economic justification to select the most advantageous solution. This paper aims to collect possible navigation methods that can provide correction for inertial navigation and to evaluate their suitability for use on a manoeuvring tactical missile. Design/methodology/approach The review of existing munitions was based on data collected from the literature and online databases. The data collected included dimensions, performance, applied navigation and guidance methods and their achievable accuracy. The requirements and limitations identified were confronted with the range of sensor parameters available on the market. Based on recent literature, navigation methods were reviewed and evaluated for applicability to inertial navigation system (INS) correction in global navigation satellite system-denied space. Findings The performance analysis of existing munition shows that small and relatively inexpensive micro-electro-mechanical system-type inertial sensors are required. A review of the parameters of existing devices of this type has shown that they are subject to measurement errors that do not allow them to achieve the delivery accuracy expected of precision missiles. The most promising navigation correction methods for manoeuvring flying objects have been identified. Originality/value The information presented in this paper is the result of the first phase of a project and presents the results of the requirements selection, initial sizing and preliminary design of the navigation system. This paper combines a review of the current state of the art in missile systems and an analysis of INS accuracy including the selection of sensor parameters.

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Section: Review Of Alternative Correction Methodsmentioning

confidence: 99%

GNSS denied navigation system for the manoeuvring flying objects

Borodacz,

Szczepański

2023

AEAT

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“…A wide range of past work has looked and image processing to aid navigation [4], [6] and some specifically leveraged ROS and associated simulation capabilities for development prior to hardware implementations [25]. The environment presented in this paper includes highly flexible image model framework, discussed in section III, and is intended to increase the fidelity of the referenced prior works and allow their expansion to include seasonal variations, multi-spectral matching, e.g., SAR-to-EO [26] or SAR-to-IR [27], and more.…”

Section: B Image Processing and Navigationmentioning

confidence: 99%

ROS georegistration: Aerial Multi-spectral Image Simulator for the Robot Operating System

Willis¹,

Brink²,

Dipple³

2022

Preprint

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This article describes a software package called ROSgeoregistration intended for use with the Robot Operating System (ROS) and the Gazebo 3D simulation environment. ROSgeoregistration provides tools for the simulation, test and deployment of aerial georegistration algorithms and is made available with a link provided in the paper. A model creation package is provided which downloads multi-spectral images from the Google Earth Engine database and, if necessary, incorporates these images into a single, possibly very large, reference image. Additionally a Gazebo plugin which uses the real-time sensor pose and image formation model to generate simulated imagery using the specified reference image is provided along with related plugins for UAV relevant data. The novelty of this work is threefold: (1) this is the first system to link the massive multi-spectral imaging database of Google's Earth Engine to the Gazebo simulator, (2) this is the first example of a system that can simulate geospatially and radiometrically accurate imagery from multiple sensor views of the same terrain region, and (3) integration with other UAS tools creates a new holistic UAS simulation environment to support UAS system and subsystem development where real-world testing would generally be prohibitive. Sensed imagery and ground truth registration information is published to client applications which can receive imagery synchronously with telemetry from other payload sensors, e.g., IMU, GPS/GNSS, barometer, and windspeed sensor data. To highlight functionality, we demonstrate ROSgeoregistration for simulating Electro-Optical (EO) and Synthetic Aperture Radar (SAR) image sensors and an example use case for developing and evaluating image-based UAS position feedback, i.e., pose for imagebased Guidance Navigation and Control (GNC) applications.

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“…In this work, we formulate a tiered optimization strategy to address some of those challenges and afford extreme-scale navigation and mapping contexts while operating with limited communication and computational resources. This effort is an adaptation of the cooperative navigation concept applied in [13] where IMU and camera data are fused into a high rate filter for a relative navigation framework that occasionally resets the filter reference frame to a new "keyframe" [14]. In this case, the high rate filter produces a marginalized delta-pose estimate, which represents the change in position and orientation, and covariance, the associated uncertainty between one keyframe and the subsequent keyframe.…”

Section: Introductionmentioning

confidence: 99%

“…When done well, the marginalized delta-pose measurements, effectively a low rate odometry measurement, and uncertainties provide an accurate representation of how the vehicle has moved over time [13]. They are also extremely compact compared to the large amounts of IMU, camera, or other data inputs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Maplets: An Efficient Approach for Cooperative SLAM Map Building Under Communication and Computation Constraints

Brink¹,

Zhang²,

Willis³

et al. 2020

Preprint

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This article introduces an approach to facilitate cooperative exploration and mapping of large-scale, near-ground, underground, or indoor spaces via a novel integration framework for locally-dense agent map data. The effort targets limited Size, Weight, and Power (SWaP) agents with an emphasis on limiting required communications and redundant processing. The approach uses a unique organization of batch optimization engines to enable a highly efficient two-tier optimization structure. Tier I consists of agents which create and potentially share local maplets (local maps, limited in size) which are generated using Simultaneous Localization and Mapping (SLAM) map-building software and then marginalized to a more compact parameterization. Maplets are generated in an overlapping manner and used to estimate the transform and uncertainty between those overlapping maplets, providing an accurate and compact odometry or deltapose representation between maplet's local frames. The delta poses can be shared between agents, and in cases where maplets have salient features (for loop closures), the compact representation of the maplet can also be shared.The second optimization tier consists of a global optimizer that seeks to optimize those maplet-to-maplet transformations, including any loop closures identified. This can provide an accurate global "skeleton" of the traversed space without operating on the high density point cloud. This compact version of the map data allows for scalable, cooperative exploration with limited communication requirements where most of the individual maplets, or low fidelity renderings, are only shared if desired.

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Relative navigation of fixed‐wing aircraft in GPS‐denied environments

Cited by 16 publications

References 29 publications

GNSS denied navigation system for the manoeuvring flying objects

GNSS denied navigation system for the manoeuvring flying objects

ROS georegistration: Aerial Multi-spectral Image Simulator for the Robot Operating System

Maplets: An Efficient Approach for Cooperative SLAM Map Building Under Communication and Computation Constraints

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